Search Results (32)

Synthetic Aperture Radar (SAR) enables all-weather maritime surveillance, yet ship-oriented bounding box (OBB) detection remains challenging in complex scenes. Strong sea clutter and dense harbor scatterers often mask the slender characteristics of ships as well as the weak responses of small ships. Meanwhile, the periodicity of angle parameterization introduces regression discontinuities, and near-symmetric, bright-scatterer-dominated signatures further cause heading ambiguity, undermining the stability of orientation prediction. Moreover, in most detectors, multi-scale feature fusion and angle estimation lack explicit coordination, and rotated-box localization performance is often jointly affected by feature degradation and unstable orientation prediction. To this end, we propose a unified framework that simultaneously strengthens multi-scale representations and stabilizes orientation modeling. Specifically, we design a Gaussian-Guided Stage-Aware Deformable Feature Pyramid Network (GSDFPN) and a Coarse-to-Fine Unit-Circle Resolver (CF-UCR). GSDFPN enhances multi-scale fusion with two plug-in components: (i) a Gaussian-guided High-level Semantic Refinement Module (GHSRM) that suppresses clutter-dominated semantics while strengthening ship-responsive cues, and (ii) a Stage-aware Deformable Fusion Module (SDFM) for low-level features, which disentangles channels into a geometry-preserving spatial stream and a clutter-resistant semantic stream, and couples them via deformable interaction with bidirectional cross-stream gating to better capture the inherent slender characteristics of ships and localize small ships. For orientation, CF-UCR decomposes angle prediction into direction-cluster classification and intra-cluster residual regression on the unit circle, effectively mitigating periodicity-induced discontinuities and stabilizing rotated-box estimation. On SSDD+ and RSDD, our method achieves AP/AP₅₀/AP₇₅ of 0.5390/0.9345/0.4529 and 0.4895/0.9210/0.4712, respectively, while reaching AP_s75/AP_m75/AP_l75 of 0.5614/0.8300/0.8392 and 0.4986/0.8163/0.8934, evidencing strong rotated-box localization across target scales in complex maritime scenes. Full article

Defect detection on Printed Circuit Boards (PCBs) constitutes a pivotal component of the quality control system in electronics manufacturing. However, owing to the intricate circuitry structures on PCB surfaces and the characteristics of defects—specifically their minute scale, irregular morphology, and susceptibility to background texture interference—existing generic deep learning models frequently fail to achieve an optimal equilibrium between detection accuracy and inference speed. To address these challenges, this study proposes MDEB-YOLO, a lightweight real-time detection network tailored for PCB micro-defects. First, to enhance the model’s perceptual capability regarding subtle geometric variations along conductive line edges, we designed the Efficient Multi-scale Deformable Attention (EMDA) module within the backbone network. By integrating parallel cross-spatial channel learning with deformable offset networks, this module achieves adaptive extraction of irregular concave–convex defect features while effectively suppressing background noise. Second, to mitigate feature loss of micro-defects during multi-scale transformations, a Bidirectional Residual Multi-scale Feature Pyramid Network (BRM-FPN) is proposed. Utilizing bidirectional weighted paths and residual attention mechanisms, this network facilitates the efficient fusion of multi-view features, significantly enhancing the representation of small targets. Finally, the detection head is reconstructed based on grouped convolution strategies to design the Lightweight Grouped Convolution Head (LGC-Head), which substantially reduces parameter volume and computational complexity while maintaining feature discriminability. The validation results on the PKU-Market-PCB dataset demonstrate that MDEB-YOLO achieves a mean Average Precision (mAP) of 95.9%, an inference speed of 80.6 FPS, and a parameter count of merely 7.11 M. Compared to baseline models, the mAP is improved by 1.5%, while inference speed and parameter efficiency are optimized by 26.5% and 24.5%, respectively; notably, detection accuracy for challenging mouse bite and spur defects increased by 3.7% and 4.0%, respectively. The experimental results confirm that the proposed method outperforms state-of-the-art approaches in both detection accuracy and real-time performance, possessing significant value for industrial applications. Full article

Galvanized steel sheets are vital anti-corrosion materials, yet their surface quality is prone to defects that impact performance. Manual inspection is inefficient, while conventional machine vision struggles with complex, small-scale defects in industrial settings. Although deep learning offers promising solutions, standard object detection models like YOLOv5 (which is short for ‘You Only Look Once’) exhibit limitations in handling the subtle textures, scale variations, and reflective surfaces characteristic of galvanized sheet defects. To address these challenges, this paper proposes S-C-B-YOLO, an enhanced detection model based on YOLOv5. First, a Squeeze-and-Excitation (SE) attention mechanism is integrated into the deep layers of the backbone network to adaptively recalibrate channel-wise features, improving focus on defect-relevant information. Second, a Transformer block is combined with a C3 module to form a C3TR module, enhancing the model’s ability to capture global contextual relationships for irregular defects. Finally, the original path aggregation network (PANet) is replaced with a bidirectional feature pyramid network (Bi-FPN) to facilitate more efficient multi-scale feature fusion, significantly boosting sensitivity to small defects. Extensive experiments on a dedicated galvanized sheet defect dataset show that S-C-B-YOLO achieves a mean average precision (mAP@0.5) of 92.6% and an inference speed of 62 FPS, outperforming several baseline models including YOLOv3, YOLOv7, and Faster R-CNN. The proposed model demonstrates a favorable balance between accuracy and speed, offering a robust and practical solution for automated, real-time defect inspection in galvanized steel production. Full article

Spectrum prediction is essential for cognitive radio, enabling dynamic management and enhanced utilization, particularly in multi-band environments. Yet, its complex spatiotemporal nature and non-stationarity pose significant challenges for achieving high accuracy. Motivated by this, we propose a multi-scale Mamba-based multi-band spectrum prediction method. The core Mamba module combines Bidirectional Selective State Space Models (SSMs) for long-range dependencies and dynamic convolution for local features, efficiently extracting spatiotemporal characteristics. A multi-scale pyramid and adaptive prediction head select appropriate feature levels per prediction step, avoiding full-sequence processing to ensure accuracy while reducing computational cost. Experiments on real-world datasets across multiple frequency bands demonstrate effective handling of spectrum non-stationarity. Compared to baseline models, the method reduces root mean square error (RMSE) by 14.9% (indoor) and 7.9% (outdoor) while cutting GPU memory by 17%. Full article

Anthracnose, a significant disease of aloe with characteristics of contact transmission, poses a considerable threat to the economic viability of aloe cultivation. To address the challenges of accurately detecting and classifying crop diseases in complex environments, this study proposes an enhanced algorithm, YOLOv11-seg-DEDB, based on the improved YOLOv11-seg model. This approach integrates multi-scale feature enhancement and a dynamic attention mechanism, aiming to achieve precise segmentation of aloe anthracnose lesions and effective disease level discrimination in complex scenarios. Specifically, a novel Disease Enhance attention mechanism is introduced, combining spatial attention and max pooling to improve the accuracy of lesion segmentation. Additionally, the DCNv2 is incorporated into the network neck to enhance the model’s ability to extract multi-scale features from targets in challenging environments. Furthermore, the Bidirectional Feature Pyramid Network structure, which includes an additional p2 detection head, replaces the original PANet network. A more lightweight detection head structure is designed, utilizing grouped convolutions and structural simplifications to reduce both the parameter count and computational load, thereby enhancing the model’s inference capability, particularly for small lesions. Experiments were conducted using a self-collected dataset of aloe anthracnose infected leaves. The results demonstrate that, compared to the original model, the improved YOLOv11-seg-DEDB model improves segmentation accuracy and mAP@50 for infected lesions by 5.3% and 3.4%, respectively. Moreover, the model size is reduced from 6.0 MB to 4.6 MB, and the number of parameters is decreased by 27.9%. YOLOv11-seg-DEDB outperforms other mainstream segmentation models, providing a more accurate solution for aloe disease segmentation and grading, thereby offering farmers and professionals more reliable disease detection outcomes. Full article

Underwater images often exhibit characteristics such as low contrast, blurred and small targets, object clustering, and considerable variations in object morphology. Traditional detection methods tend to be susceptible to omission and false positives under these circumstances. Furthermore, owing to the constrained memory and limited computing power of underwater robots, there is a significant demand for lightweight models in underwater object detection tasks. Therefore, we propose an enhanced lightweight YOLOv10n-based model, BSE-YOLO. Firstly, we replace the original neck with an improved Bidirectional Feature Pyramid Network (Bi-FPN) to reduce parameters. Secondly, we propose a Multi-Scale Attention Synergy Module (MASM) to enhance the model’s perception of difficult features and make it focus on the important regions. Finally, we integrate Efficient Multi-Scale Attention (EMA) into the backbone and neck to improve feature extraction and fusion. The experiment results demonstrate that the proposed BSE-YOLO reaches 83.7% mAP@0.5 on URPC2020 and 83.9% mAP@0.5 on DUO, with the parameters reducing 2.47 M. Compared to the baseline model YOLOv10n, our BSE-YOLO improves mAP@0.5 by 2.2% and 3.0%, respectively, while reducing the number of parameters by approximately 0.2 M. The BSE-YOLO achieves a good balance between accuracy and lightweight, providing an effective solution for underwater object detection. Full article

Background: Despite advances in artificial intelligence (AI) for Diabetic Retinopathy (DR) classification, traditional severity-based approaches often lack interpretability and fail to capture specific lesion-centered characteristics. To address these limitations, we constructed the National Medical Center (NMC) dataset, independently annotated by medical professionals with detailed labels of major DR lesions, including retinal hemorrhages, microaneurysms, and exudates. Methods: This study explores four critical research questions. First, we assess the analytical advantages of lesion-centered labeling compared to traditional severity-based labeling. Second, we investigate the potential complementarity between these labeling approaches through integration experiments. Third, we analyze how various model architectures and classification strategies perform under different labeling schemes. Finally, we evaluate decision-making differences between labeling methods using visualization techniques. We benchmarked the lesion-centered NMC dataset against the severity-based public Asia Pacific Tele-Ophthalmology Society (APTOS) dataset, conducting experiments with EfficientNet—a convolutional neural network architecture—and diverse classification strategies. Results: Our results demonstrate that binary classification effectively identifies severe non-proliferative Diabetic Retinopathy (Severe NPDR) exhibiting complex lesion patterns, while relationship-based learning enhances performance for underrepresented classes. Transfer learning from NMC to APTOS notably improved severity classification, achieving performance gains of 15.2% in mild cases and 66.3% in severe cases through feature fusion using Bidirectional Feature Pyramid Network (BiFPN) and Feature Pyramid Network (FPN). Visualization results confirmed that lesion-centered models focus more precisely on pathological features. Conclusions: Our findings highlight the benefits of integrating lesion-centered and severity-based information to enhance both accuracy and interpretability in DR classification. Future research directions include spatial lesion mapping and the development of clinically grounded learning methodologies. Full article

With the accelerated transition of the global energy structure towards decarbonization, the share of PV power generation in the power system continues to rise. IEA predicts PV will account for 80% of new global renewable installations during 2025–2030. However, latent faults emerging from the long-term operation of photovoltaic (PV) power plants significantly compromise their operational efficiency. The existing EL detection methods in PV plants face challenges including grain boundary interference, probe band artifacts, non-uniform luminescence, and complex backgrounds, which elevate the risk of missing small defects. In this paper, we propose a high-precision defect detection method based on BiFDRep-YOLOv8n for small target defects in photovoltaic (PV) power plants, aiming to improve the detection accuracy and real-time performance and to provide an efficient solution for the intelligent detection of PV power plants. Firstly, the visual transformer RepViT is constructed as the backbone network, based on the dual-path mechanism of Token Mixer and Channel Mixer, to achieve local feature extraction and global information modeling, and combined with the structural reparameterization technique, to enhance the sensitivity of detecting small defects. Secondly, for the multi-scale characteristics of defects, the neck network is optimized by introducing a bidirectional weighted feature pyramid network (BiFPN), which adopts an adaptive weight allocation strategy to enhance feature fusion and improve the characterization of defects at different scales. Finally, the detection head part uses DyHead-DCNv3, which combines the triple attention mechanism of scale, space, and task awareness, and introduces deformable convolution (DCNv3) to improve the modeling capability and detection accuracy of irregular defects. Full article

Aiming at the demand for defect detection accuracy and efficiency under the trend of high-density and integration in printed circuit board (PCB) manufacturing, this paper proposes an improved YOLOv8n model (YOLO-SUMAS), which enhances detection performance through multi-module collaborative optimization. The model introduces the SCSA attention mechanism, which improves the feature expression capability through spatial and channel synergistic attention; adopts the Unified-IoU loss function, combined with the dynamic bounding box scaling and bi-directional weight allocation strategy, to optimize the accuracy of high-quality target localization; integrates the MobileNetV4 lightweight architecture and its MobileMQA attention module, which reduces the computational complexity and improves the inference speed; and combines ASF-SDI Neck structure with weighted bi-directional feature pyramid and multi-level semantic detail fusion to strengthen small target detection capability. The experiments are based on public datasets, and the results show that the improved model achieves 98.8% precision and 99.2% recall, and mAP@50 reached 99.1%, significantly better than the original YOLOv8n and other mainstream models. YOLO-SUMAS provides a highly efficient industrial-grade PCB defect detection solution by considering high precision and real-time performance while maintaining lightweight characteristics. Full article

The acquisition of approaching vessels’ information is a critical technological challenge for maritime risk warning and intelligent collision avoidance decision-making. This paper proposes a method for autonomously identifying types of approaching vessels based on an improved YOLOv8 model and ship light features, aiming to infer the propulsion mode, size, movement, and operational nature of the approaching vessels in real-time through the color, quantity, and spatial distribution of lights. Firstly, to address the challenges of the small target characteristics of ship lights and complex environmental interference, an improved YOLOv8 model is developed: The dilation-wise residual (DWR) module is introduced to optimize the feature extraction capability of the C2f structure. The bidirectional feature pyramid network (BiFPN) is adopted to enhance multi-scale feature fusion. A hybrid attention transformer (HAT) is employed to enhance the small target detection capability of the detection head. This framework achieves precise ship light recognition under complex maritime circumstances. Secondly, 23 spatio-semantic feature indicators are established to encode ship light patterns, and a multi-viewing angle dataset is constructed. This dataset covers 36 vessel types under four viewing angles (front, port-side, starboard, and stern viewing angles), including the color, quantity, combinations, and spatial distribution of the ship lights. Finally, a two-stage discriminative model is proposed: ECA-1D-CNN is utilized for the rapid assessment of the viewing angle of the vessel. Deep learning algorithms are dynamically applied for vessel type determination within the assessed viewing angles. Experimental results show that this method achieves high determination accuracy. This paper provides a kind of technical support for intelligent situational awareness and the autonomous collision avoidance of ships. Full article

Fire detection presents considerable challenges due to the destructive and unpredictable characteristics of fires. These difficulties are amplified by the small size and low-resolution nature of fire and smoke targets in images captured from a distance, making it hard for models to extract relevant features. To address this, we introduce a novel method for small-target fire and smoke detection named YOLOv7scb. This approach incorporates two key improvements to the YOLOv7 framework: the use of space-to-depth convolution (SPD-Conv) and C3 modules, enhancing the model’s ability to extract features from small targets effectively. Additionally, a weighted bidirectional feature pyramid network (BiFPN) is integrated into the feature-extraction network to merge features across scales efficiently without increasing the model’s complexity. We also replace the conventional complete intersection over union (CIoU) loss function with Focal-CIoU, which reduces the degrees of freedom in the loss function and improves the model’s robustness. Given the limited size of the initial fire and smoke dataset, a transfer-learning strategy is applied during training. Experimental results demonstrate that our proposed model surpasses others in metrics such as precision and recall. Notably, it achieves a precision of 98.8% for small-target flame detection and 90.6% for small-target smoke detection. These findings underscore the model’s effectiveness and its broad potential for fire detection and mitigation applications. Full article

In order to enhance the precision of UAV (unmanned aerial vehicle) landings and realize the convenient and rapid deployment of the model to the mobile terminal, this study proposes a Land-YOLO lightweight UAV-guided landing algorithm based on the YOLOv8 n model. Firstly, GhostConv replaces standard convolutions in the backbone network, leveraging existing feature maps to create additional “ghost” feature maps via low-cost linear transformations, thereby lightening the network structure. Additionally, the CSP structure of the neck network is enhanced by incorporating the PartialConv structure. This integration allows for the transmission of certain channel characteristics through identity mapping, effectively reducing both the number of parameters and the computational load of the model. Finally, the bidirectional feature pyramid network (BiFPN) module is introduced, and the accuracy and average accuracy of the model recognition landing mark are improved through the bidirectional feature fusion and weighted fusion mechanism. The experimental results show that for the landing-sign data sets collected in real and virtual environments, the Land-YOLO algorithm in this paper is 1.4% higher in precision and 0.91% higher in mAP0.5 than the original YOLOv8n baseline, which can meet the detection requirements of landing signs. The model’s memory usage and floating-point operations per second (FLOPs) have been reduced by 42.8% and 32.4%, respectively. This makes it more suitable for deployment on the mobile terminal of a UAV. Full article

The current object detection algorithm based on CNN makes it difficult to effectively capture the characteristics of subtle defects in online industrial product packaging bags. These defects are often visually similar to the texture or background of normal product packaging bags, and the model cannot effectively distinguish them. In order to deal with these challenges, this paper optimizes and improves the network structure based on YOLOv8 to achieve accurate identification of defects. First, in order to solve the long-tail distribution problem of data, a fuzzy search data enhancement algorithm is introduced to effectively increase the number of samples. Secondly, a joint network of FasterNet and SPD-Conv is proposed to replace the original backbone network of YOLOv8, which effectively reduces the computing load and improves the accuracy of defect identification. In addition, in order to further improve the performance of multiscale feature fusion, a weighted bidirectional feature pyramid network (BiFPN) is introduced, which effectively enhances the model’s ability to detect defects at different scales through the fusion of deep information and shallow information. Finally, in order to reduce the sensitivity of the defect position deviation, the NWD loss function is used to optimize the positioning performance of the model better and reduce detection errors caused by position errors. Experimental results show that the FSNB_YOLOv8 model proposed in this paper can reach 98.8% mAP50 accuracy. This success not only verifies the effectiveness of the optimization and improvement of this article’s model but also provides an efficient and accurate solution for surface defect detection of industrial product packaging bags on artificial assembly systems. Full article

Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method. Full article

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network’s attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications. Full article