Search Results (9)

Black fungus cultivation in bagged form requires frequent inspection of mycelial growth, a process that is labor-intensive and susceptible to subjective judgment. In addition, timely detection of contamination in low-light and high-humidity environments remains a significant challenge. To address these issues, this paper proposed an intelligent agriculture system for black fungus cultivation, with emphasis on practical deployment under real farming conditions. The system integrates a private 5G network with a YOLOv8-based deep learning model for real-time object detection and growth monitoring. Continuous image acquisition and data feedback are achieved through a multi-parameter environmental sensing module and an autonomous ground vehicle (AGV) equipped with IP cameras. To improve model robustness, more than 42,000 labeled training images were generated through data augmentation, and a modified C2f network architecture was employed. Experimental results show that the model achieved a detection accuracy of 93.7% with an average confidence score of 0.96 under live testing conditions. The deployed 5G network provided a downlink throughput of 645.2 Mbps and an uplink throughput of 147.5 Mbps, ensuring sufficient bandwidth and low latency for real-time inference and transmission. Field trials conducted over five cultivation batches demonstrated improvements in disease detection, reductions in labor requirements, and an increase in the average yield success rate to 80%. These findings indicate that the proposed method offers a scalable and practical solution for precision agriculture, integrating next-generation communication technologies with deep learning to enhance cultivation management. Full article

Conventional inspections in car damage assessments depend on visual judgments by human inspectors, which are labor-intensive and prone to fraudulent practices through manipulating damages. Recent advancements in artificial intelligence have given rise to a state-of-the-art object detection algorithm, the You Only Look Once algorithm (YOLO), that sets a new standard in smart and automated damage assessment. This study proposes an enhanced YOLOv9 network tailored to detect six types of car damage. The enhancements include the convolutional block attention module (CBAM), applied to the backbone layer to enhance the model’s ability to focus on key damaged regions, and the SCYLLA-IoU (SIoU) loss function, introduced for bounding box regression. To be able to assess the damage severity comprehensively, we propose a novel formula named damage severity index (DSI) for quantifying damage severity directly from images, integrating multiple factors such as the number of detected damages, the ratio of damage to the image size, object detection confidence, and the type of damage. Experimental results on the CarDD dataset show that the proposed model outperforms state-of-the-art YOLO algorithms by 1.75% and that the proposed DSI demonstrates intuitive assessment of damage severity with numbers, aiding repair decisions. Full article

In the field of hyperspectral video tracking (HVT), occclusion poses a challenging issue without a satisfactory solution. To address this challenge, the current study explores the application of capsule networks in HVT and proposes an approach based on octave convolution and a spatial–spectral capsule network (OCSCNet). Specifically, the spatial–spectral octave convolution module is designed to learn features from hyperspectral images by integrating spatial and spectral information. Hence, unlike traditional convolution, which is limited to learning spatial features, the proposed strategy also focuses on learning and modeling the spectral features. The proposed spatial–spectral capsule network integrates spectral information to distinguish among underlying capsule categories based on their spectral similarity. The approach enhances separability and establishes relationships between different components and targets at various scales. Finally, a confidence threshold judgment module utilizes the information from the initial and adjacent frames for relocating the lost target. Experiments conducted on the HOT2023 dataset illustrate that the proposed model outperforms state-of-the-art methods, achieving a success rate of 65.2% and a precision of 89.3%. In addition, extensive experimental results and visualizations further demonstrate the effectiveness and interpretability of the proposed OCSCNet. Full article

In response to the numerous challenges faced by traditional human pose recognition methods in practical applications, such as dense targets, severe edge occlusion, limited application scenarios, complex backgrounds, and poor recognition accuracy when targets are occluded, this paper proposes a YOLO-Pose algorithm for human pose estimation. The specific improvements are divided into four parts. Firstly, in the Backbone section of the YOLO-Pose model, lightweight GhostNet modules are introduced to reduce the model’s parameter count and computational requirements, making it suitable for deployment on unmanned aerial vehicles (UAVs). Secondly, the ACmix attention mechanism is integrated into the Neck section to improve detection speed during object judgment and localization. Furthermore, in the Head section, key points are optimized using coordinate attention mechanisms, significantly enhancing key point localization accuracy. Lastly, the paper improves the loss function and confidence function to enhance the model’s robustness. Experimental results demonstrate that the improved model achieves a 95.58% improvement in mAP50 and a 69.54% improvement in mAP50-95 compared to the original model, with a reduction of 14.6 M parameters. The model achieves a detection speed of 19.9 ms per image, optimized by 30% and 39.5% compared to the original model. Comparisons with other algorithms such as Faster R-CNN, SSD, YOLOv4, and YOLOv7 demonstrate varying degrees of performance improvement. Full article

Trackers based on the Siamese network have received much attention in recent years, owing to its remarkable performance, and the task of object tracking is to predict the location of the target in current frame. However, during the tracking process, distractors with similar appearances affect the judgment of the tracker and lead to tracking failure. In order to solve this problem, we propose a Siamese visual tracker with spatial-channel attention and a ranking head network. Firstly, we propose a Spatial Channel Attention Module, which fuses the features of the template and the search region by capturing both the spatial and the channel information simultaneously, allowing the tracker to recognize the target to be tracked from the background. Secondly, we design a ranking head network. By introducing joint ranking loss terms including classification ranking loss and confidence&IoU ranking loss, classification and regression branches are linked to refine the tracking results. Through the mutual guidance between the classification confidence score and IoU, a better positioning regression box is selected to improve the performance of the tracker. To better demonstrate that our proposed method is effective, we test the proposed tracker on the OTB100, VOT2016, VOT2018, UAV123, and GOT-10k testing datasets. On OTB100, the precision and success rate of our tracker are 0.925 and 0.700, respectively. Considering accuracy and speed, our method, overall, achieves state-of-the-art performance. Full article

The advent of hyperspectral cameras has popularized the study of hyperspectral video trackers. Although hyperspectral images can better distinguish the targets compared to their RGB counterparts, the occlusion and rotation of the target affect the effectiveness of the target. For instance, occlusion obscures the target, reducing the tracking accuracy and even causing tracking failure. In this regard, this paper proposes a novel hyperspectral video tracker where the double Siamese network (D-Siam) forms the basis of the framework. Moreover, AlexNet serves as the backbone of D-Siam. The current study also adopts a novel spectral–deviation-based dimensionality reduction approach on the learned features to match the input requirements of the AlexNet. It should be noted that the proposed dimensionality reduction method increases the distinction between the target and background. The two response maps, namely the initial response map and the adjacent response map, obtained using the D-Siam network, were fused using an adaptive weight estimation strategy. Finally, a confidence judgment module is proposed to regulate the update for the whole framework. A comparative analysis of the proposed approach with state-of-the-art trackers and an extensive ablation study were conducted on a publicly available benchmark hyperspectral dataset. The results show that the proposed tracker outperforms the existing state-of-the-art approaches against most of the challenges. Full article

The occlusion of cloud layers affects the accurate acquisition of ground object information and causes a large amount of useless remote-sensing data transmission and processing, wasting storage, as well as computing resources. Therefore, in this paper, we designed a lightweight composite neural network model to calculate the cloud amount in high-resolution visible remote-sensing images by training the model using thumbnail images and browsing images in remote-sensing images. The training samples were established using paired thumbnail images and browsing images, and the cloud-amount calculation model was obtained by training a proposed composite neural network. The strategy used the thumbnail images for preliminary judgment and the browsing images for accurate calculation, and this combination can quickly determine the cloud amount. The multi-scale confidence fusion module and bag-of-words loss function were redesigned to achieve fast and accurate calculation of cloud-amount data from remote-sensing images. This effectively alleviates the problem of low cloud-amount calculation, thin clouds not being counted as clouds, and that of ice and clouds being confused as in existing methods. Furthermore, a complete dataset of cloud-amount calculation for remote-sensing images, CTI_RSCloud, was constructed for training and testing. The experimental results show that, with less than 13 MB of parameters, the proposed lightweight network model greatly improves the timeliness of cloud-amount calculation, with a runtime is in the millisecond range. In addition, the calculation accuracy is better than the classic lightweight networks and backbone networks of the best cloud-detection models. Full article

Object sounds can enhance the attentional selection and perceptual processing of semantically-related visual stimuli. However, it is currently unknown whether crossmodal semantic congruence also affects the post-perceptual stages of information processing, such as short-term memory (STM), and whether this effect is modulated by the object consistency with the background visual scene. In two experiments, participants viewed everyday visual scenes for 500 ms while listening to an object sound, which could either be semantically related to the object that served as the STM target at retrieval or not. This defined crossmodal semantically cued vs. uncued targets. The target was either in- or out-of-context with respect to the background visual scene. After a maintenance period of 2000 ms, the target was presented in isolation against a neutral background, in either the same or different spatial position as in the original scene. The participants judged the same vs. different position of the object and then provided a confidence judgment concerning the certainty of their response. The results revealed greater accuracy when judging the spatial position of targets paired with a semantically congruent object sound at encoding. This crossmodal facilitatory effect was modulated by whether the target object was in- or out-of-context with respect to the background scene, with out-of-context targets reducing the facilitatory effect of object sounds. Overall, these findings suggest that the presence of the object sound at encoding facilitated the selection and processing of the semantically related visual stimuli, but this effect depends on the semantic configuration of the visual scene. Full article

Previous studies on metacognitive performance have explored children’s abilities during primary school (7–11 years) in abstract and mathematical reasoning tasks. However, there have been no studies evaluating the metamemory processes with spatial tasks in primary school children, and even more generally, only a few studies have explored spatial metacognition in adults. Taking as a preliminary study a Spanish school, the present work explores the validity of the confidence judgment model when thinking about one’s own performance in a spatial test, for boys and girls in Second Year of Primary Education (mean age of 7 years). A total of 18 boys and 15 girls applied a 4-point scale to evaluate, item by item, the confidence of their responses in the Spatial aptitude test “E” of the EFAI-1 (Factorial Assessment of Intellectual Abilities to mentally process visual stimuli). Accessibility and Accuracy Indexes were calculated for each item of the spatial task. The effect of gender was analyzed too. The tasks were administered in small groups; at the end examiners interviewed each participant, performing the confidence judgment task, item by item, of the EFAI-1 previously answered. The results (analyses carried out by SPSS) showed a high mean confidence (3 mean points out of a maximum of 4), without finding any significant differences either in the spatial performance or in the mean confidence rating between boys and girls. A significant relationship between confidence judgments and spatial task performance accuracy was found. The relationship between confidence judgments and spatial performance cannot be confirmed. The procedure adapted for testing spatial judgments about the own responses has been useful for showing the well calibrated perception about performance at this stage. The implications of the results of this exploratory study and the potential of the application of the procedure to promote thought about one’s own spatial performance and the development of strategies that modulate the effective approach of this type of spatial tasks are discussed within an educational approach. Full article