Search Results (17)

Accurate detection and counting of rice ears serve as a critical indicator for yield estimation, but the complex conditions of paddy fields limit the efficiency and precision of traditional sampling methods. We propose REU-YOLO, a model specifically designed for UAV low-altitude remote sensing to collect images of rice ears, to address issues such as high-density and complex spatial distribution with occlusion in field scenes. Initially, we combine the Additive Block containing Convolutional Additive Self-attention (CAS) and Convolutional Gated Linear Unit (CGLU) to propose a novel module called Additive-CGLU-C2F (AC-C2f) as a replacement for the original C2f in YOLOv8. It can capture the contextual information between different regions of images and improve the feature extraction ability of the model, introduce the Dropblock strategy to reduce model overfitting, and replace the original SPPF module with the SPPFCSPC-G module to enhance feature representation and improve the capacity of the model to extract features across varying scales. We further propose a feature fusion network called Multi-branch Bidirectional Feature Pyramid Network (MBiFPN), which introduces a small object detection head and adjusts the head to focus more on small and medium-sized rice ear targets. By using adaptive average pooling and bidirectional weighted feature fusion, shallow and deep features are dynamically fused to enhance the robustness of the model. Finally, the Inner-PloU loss function is introduced to improve the adaptability of the model to rice ear morphology. In the self-developed dataset UAVR, REU-YOLO achieves a precision (P) of 90.76%, a recall (R) of 86.94%, an mAP_0.5 of 93.51%, and an mAP_0.5:0.95 of 78.45%, which are 4.22%, 3.76%, 4.85%, and 8.27% higher than the corresponding values obtained with YOLOv8 s, respectively. Furthermore, three public datasets, DRPD, MrMT, and GWHD, were used to perform a comprehensive evaluation of REU-YOLO. The results show that REU-YOLO indicates great generalization capabilities and more stable detection performance. Full article

Accurate detection of tea shoots in natural environments is crucial for facilitating intelligent tea picking, field management, and automated harvesting. However, the detection performance of existing methods in complex scenes remains limited due to factors such as the small size, high density, severe overlap, and the similarity in color between tea shoots and the background. Consequently, this paper proposes an improved target detection algorithm, RFA-YOLOv8, based on YOLOv8, which aims to enhance the detection accuracy and robustness of tea shoots in natural environments. First, a self-constructed dataset containing images of tea shoots under various lighting conditions is created for model training and evaluation. Second, the multi-scale feature extraction capability of the model is enhanced by introducing RFCAConv along with the optimized SPPFCSPC module, while the spatial perception ability is improved by integrating the RFAConv module. Finally, the EIoU loss function is employed instead of CIoU to optimize the accuracy of the bounding box positioning. The experimental results demonstrate that the improved model achieves 84.1% and 58.7% in mAP@0.5 and mAP@0.5:0.95, respectively, which represent increases of 3.6% and 5.5% over the original YOLOv8. Robustness is evaluated under strong, moderate, and dim lighting conditions, yielding improvements of 6.3% and 7.1%. In dim lighting, mAP@0.5 and mAP@0.5:0.95 improve by 6.3% and 7.1%, respectively. The findings of this research provide an effective solution for the high-precision detection of tea shoots in complex lighting environments and offer theoretical and technical support for the development of smart tea gardens and automated picking. Full article

Accurate detection of Jinxiu Malus fruits in unstructured orchard environments is hampered by frequent overlap, occlusion, and variable illumination. To address these challenges, we propose YOLOv8-MSP-PD (YOLOv8 with Multi-Scale Pyramid Fusion and Proportional Distance IoU), a lightweight model built on an enhanced YOLOv8 architecture. We replace the backbone with MobileNetV4, incorporating unified inverted bottleneck (UIB) modules and depth-wise separable convolutions for efficient feature extraction. We introduce a spatial pyramid pooling fast cross-stage partial connections (SPPFCSPC) module for multi-scale feature fusion and a modified proportional distance IoU (MPD-IoU) loss to optimize bounding-box regression. Finally, layer-adaptive magnitude pruning (LAMP) combined with knowledge distillation compresses the model while retaining performance. On our custom Jinxiu Malus dataset, YOLOv8-MSP-PD achieves a mean average precision (mAP) of 92.2% (1.6% gain over baseline), reduces floating-point operations (FLOPs) by 59.9%, and shrinks to 2.2 MB. Five-fold cross-validation confirms stability, and comparisons with Faster R-CNN and SSD demonstrate superior accuracy and efficiency. This work offers a practical vision solution for agricultural robots and guidance for lightweight detection in precision agriculture. Full article

In response to the decreased accuracy in person detection caused by densely populated areas and mutual occlusions in public spaces, a human head-detection approach is employed to assist in detecting individuals. To address key issues in dense scenes—such as poor feature extraction, rough label assignment, and inefficient pooling—we improved the YOLOv7 network in three aspects: adding attention mechanisms, enhancing the receptive field, and applying multi-scale feature fusion. First, a large amount of surveillance video data from crowded public spaces was collected to compile a head-detection dataset. Then, based on YOLOv7, the network was optimized as follows: (1) a CBAM attention module was added to the neck section; (2) a Gaussian receptive field-based label-assignment strategy was implemented at the junction between the original feature-fusion module and the detection head; (3) the SPPFCSPC module was used to replace the multi-space pyramid pooling. By seamlessly uniting CBAM, RFLAGauss, and SPPFCSPC, we establish a novel collaborative optimization framework. Finally, experimental comparisons revealed that the improved model’s accuracy increased from 92.4% to 94.4%; recall improved from 90.5% to 93.9%; and inference speed increased from 87.2 frames per second to 94.2 frames per second. Compared with single-stage object-detection models such as YOLOv7 and YOLOv8, the model demonstrated superior accuracy and inference speed. Its inference speed also significantly outperforms that of Faster R-CNN, Mask R-CNN, DINOv2, and RT-DETRv2, markedly enhancing both small-object (head) detection performance and efficiency. Full article

In response to the poor detection performance of grapes in orchards caused by issues such as leaf occlusion and fruit overlap, this study proposes an improved grape detection method named YOLOv7-MCSF based on the You Only Look Once v7 (YOLOv7) framework. Firstly, the original backbone network is replaced with MobileOne to achieve a lightweight improvement of the model, thereby reducing the number of parameters. In addition, a Channel Attention (CA) module was added to the neck network to reduce interference from the orchard background and to accelerate the inference speed. Secondly, the SPPFCSPC pyramid pooling is embedded to enhance the speed of image feature fusion while maintaining a consistent receptive field. Finally, the Focal-EIoU loss function is employed to optimize the regression prediction boxes, accelerating their convergence and improving regression accuracy. The experimental results indicate that, compared to the original YOLOv7 model, the YOLOv7-MCSF model achieves a 26.9% reduction in weight, an increase in frame rate of 21.57 f/s, and improvements in precision, recall, and mAP of 2.4%, 1.8%, and 3.5%, respectively. The improved model can efficiently and in real-time identify grape clusters, providing technical support for the deployment of mobile devices and embedded grape detection systems in orchard environments. Full article

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model’s detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production. Full article

Unauthorized unmanned aerial vehicles (UAVs) pose threats to public safety and individual privacy. Traditional object-detection approaches often fall short during their application in anti-UAV technologies. To address this issue, we propose the YOLOv7-GS model, which is designed specifically for the identification of small UAVs in complex and low-altitude environments. This research primarily aims to improve the model’s detection capabilities for small UAVs in complex backgrounds. Enhancements were applied to the YOLOv7-tiny model, including adjustments to the sizes of prior boxes, incorporation of the InceptionNeXt module at the end of the neck section, and introduction of the SPPFCSPC-SR and Get-and-Send modules. These modifications aid in the preservation of details about small UAVs and heighten the model’s focus on them. The YOLOv7-GS model achieves commendable results on the DUT Anti-UAV and the Amateur Unmanned Air Vehicle Detection datasets and performs to be competitive against other mainstream algorithms. Full article

Foreign fibers directly impact the quality of raw cotton, affecting the prices of textile products and the economic efficiency of cotton textile enterprises. The accurate differentiation and labeling of foreign fibers require domain-specific knowledge, and labeling scattered cotton foreign fibers in images consumes substantial time and labor costs. In this study, we propose a semi-supervised foreign fiber detection approach that uses unlabeled image information and a small amount of labeled data for model training. Our proposed method, Efficient YOLOv5-cotton, introduces CBAM to address the issue of the missed detection and false detection of small-sized cotton foreign fibers against complex backgrounds. Second, the algorithm designs a multiscale feature information extraction network, SPPFCSPC, which improves its ability to generalize to fibers of different shapes. Lastly, to reduce the increased network parameters and computational complexity introduced by the SPPFCSPC module, we replace the C3 layer with the C3Ghost module. We evaluate Efficient YOLOv5 for detecting various types of foreign fibers. The results demonstrate that the improved Efficient YOLOv5-cotton achieves a 1.6% increase in mAP@0.5 (mean average precision) compared with the original Efficient YOLOv5 and reduces model parameters by 10% compared to the original Efficient YOLOv5 with SPPFCSPC. Our experiments show that our proposed method enhances the accuracy of foreign fiber detection using Efficient YOLOv5-cotton and considers the trade-off between the model size and computational cost. Full article

Underwater visual detection technology is crucial for marine exploration and monitoring. Given the growing demand for accurate underwater target recognition, this study introduces an innovative architecture, YOLOv8-MU, which significantly enhances the detection accuracy. This model incorporates the large kernel block (LarK block) from UniRepLKNet to optimize the backbone network, achieving a broader receptive field without increasing the model’s depth. Additionally, the integration of C2fSTR, which combines the Swin transformer with the C2f module, and the SPPFCSPC_EMA module, which blends Cross-Stage Partial Fast Spatial Pyramid Pooling (SPPFCSPC) with attention mechanisms, notably improves the detection accuracy and robustness for various biological targets. A fusion block from DAMO-YOLO further enhances the multi-scale feature extraction capabilities in the model’s neck. Moreover, the adoption of the MPDIoU loss function, designed around the vertex distance, effectively addresses the challenges of localization accuracy and boundary clarity in underwater organism detection. The experimental results on the URPC2019 dataset indicate that YOLOv8-MU achieves an mAP@0.5 of 78.4%, showing an improvement of 4.0% over the original YOLOv8 model. Additionally, on the URPC2020 dataset, it achieves 80.9%, and, on the Aquarium dataset, it reaches 75.5%, surpassing other models, including YOLOv5 and YOLOv8n, thus confirming the wide applicability and generalization capabilities of our proposed improved model architecture. Furthermore, an evaluation on the improved URPC2019 dataset demonstrates leading performance (SOTA), with an mAP@0.5 of 88.1%, further verifying its superiority on this dataset. These results highlight the model’s broad applicability and generalization capabilities across various underwater datasets. Full article

The visual monitoring of ship-engine-room equipment is an essential component of ship-cabin intelligence. In response to issues such as imbalanced quantities of different categories of engine room equipment and severe occlusion, this paper presents improvements to YOLOv8-M. Firstly, the introduction of the SPPFCSPC module enhances the feature extraction capabilities of the backbone extraction network. Subsequently, improvements are implemented in the neck network to create GCFPN, facilitating further feature fusion, and introducing the Dynamic Head module, which fuses the deformable convolution, in the part of the detection head, so as to improve the performance of the network. Finally, the FOCAL EIOU LOSS is introduced, while mitigating the impact of dataset imbalance through class-wise data augmentation. In this paper, the ship cabin equipment dataset and the public dataset MS COCO2017 are evaluated. Compared with YOLOv8-M, the mAP50 of GCD-YOLOv8 is improved by 2.6% and 0.4%, respectively. Full article

Aiming at the problems of target detection models in traffic scenarios including a large number of parameters, heavy computational burden, and high application cost, this paper introduces an enhanced lightweight real-time detection algorithm, which exhibits higher detection speed and accuracy for vehicle detection. This paper considers the YOLOv7 algorithm as the benchmark model, designs a lightweight backbone network, and uses the MobileNetV3 lightweight network to extract target features. Inspired by the structure of SPPF, the spatial pyramid pooling module is reconfigured by incorporating GSConv, and a lightweight SPPFCSPC-GS module is designed, aiming to minimize the quantity of model parameters and enhance the training speed even further. Furthermore, the CA mechanism is integrated to enhance the feature extraction capability of the model. Finally, the MPDIoU loss function is utilized to optimize the model’s training process. Experiments showcase that the refined YOLOv7 algorithm can achieve 98.2% mAP on the BIT-Vehicle dataset with 52.8% fewer model parameters than the original model and a 35.2% improvement in FPS. The enhanced model adeptly strikes a finer equilibrium between velocity and precision, providing favorable conditions for embedding the model into mobile devices. Full article

Detecting defects in power transmission lines through unmanned aerial inspection images is crucial for evaluating the operational status of outdoor transmission equipment. This paper presents a defect recognition method called EDF-YOLOv5, which is based on the YOLOv5s, to enhance detection accuracy. Firstly, the EN-SPPFCSPC module is designed to improve the algorithm’s ability to extract information, thereby enhancing the detection performance for small target defects. Secondly, the algorithm incorporates a high-level semantic feature information extraction network, DCNv3C3, which improves its ability to generalize to defects of different shapes. Lastly, a new bounding box loss function, Focal-CIoU, is introduced to enhance the contribution of high-quality samples during training. The experimental results demonstrate that the enhanced algorithm achieves a 2.3% increase in mean average precision (mAP@.5) for power transmission line defect detection, a 0.9% improvement in F1-score, and operates at a detection speed of 117 frames per second. These findings highlight the superior performance of EDF-YOLOv5 in detecting power transmission line defects. Full article

Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect. Full article

In order to estimate bayberry yield, a lightweight bayberry target detection count model, YOLOv7-CS, based on YOLOv7, was proposed to address the issues of slow detection and recognition speed, as well as low recognition rate, of high-density bayberry targets under complex backgrounds. In this study, 8990 bayberry images were used for experiments. The training set, validation set, and test set were randomly recreated in a ratio of 8:1:1. The new network was developed with SPD-Conv detection head modules to extract features at various scales, to better capture small and indistinct bayberry targets. To improve accuracy and achieve a lightweight design, a CNxP module that replaces the backbone’s ELAN structure is proposed. We propose a global attention mechanism (GAM) in the intermediate layers of the network, to enhance cross-dimensional interactions, and a new pyramid pooling module called SPPFCSPC, to extend the field of perception and improve boundary detection accuracy. Finally, we combine the Wise-IoU function to enhance the network’s ability to identify overlapping and occluded objects. Compared with the SSD, Faster-RCNN, DSSD, and YOLOv7X target detection algorithms, YOLOv7-CS increases mAP 0.5 by 35.52%, 56.74%, 12.36%, and 7.05%. Compared with basic YOLOv7, mAP 0.5 increased from 5.43% to 90.21%, while mAP 0.95 increased from 13.2% to 54.67%. This parameter is reduced by 17.3 m. Ablation experiments further show that the designed module improves the accuracy of bayberry detection, reduces parameter counts, and makes bayberry image detection more accurate and effective. Full article

This paper proposes an Synthetic Aperture Radar (SAR) imaging and detection model of multiple targets at the maritime scene. The sea surface sample is generated according to the composite rough surface theory. The SAR imaging model is constructed based on a hybrid EM calculation approach with the fast ray tracing strategy and the modified facet Small Slope Approximation (SSA) solution. Numerical simulations calculate the EM scattering and the SAR imaging of the multiple cone targets above the sea surface, with the scattering mechanisms analyzed and discussed. The SAR imaging datasets are then set up by the SAR image simulations. A modified YOLOv7 neural network with the Spatial Pyramid Pooling Fast Connected Spatial Pyramid Convolution (SPPFCSPC) module, Convolutional Block Attention Module (CBAM), modified Feature Pyramid Network (FPN) structure and extra detection head is developed. In the training process on our constructed SAR datasets, the precision rate, recall rate, mAP@0.5 and mAP@0.5:0.95 are 97.46%, 90.08%, 92.91% and 91.98%, respectively, after 300 rounds of training. The detection results show that the modified YOLOv7 has a good performance in selecting the targets out of the complex sea surface and multipath interference background. Full article