Improved YOLOv5 Algorithm for Real-Time Prediction of Fish Yield in All Cage Schools

Wang, Lei; Chen, Ling-Zhi; Peng, Bo; Lin, Ying-Tien

doi:10.3390/jmse12020195

Cited by 3 publications

(1 citation statement)

References 30 publications

(31 reference statements)

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“…In addition to semantic segmentation methods, many researchers have recently utilized object detection deep learning methods involving YOLO (You Only Look Once) algorithms to identify and localize objects in the ocean [26][27][28][29][30][31][32][33][34][35]. The first generation YOLO model was developed by Redmon et al [36] in 2016 and was subsequently enhanced [37].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images

Dong,

Shi,

Hao

et al. 2024

JMSE

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The rapid and disordered expansion of artificial marine aquaculture areas has caused severe ecological and environmental problems. Accurate monitoring of offshore aquaculture areas is urgent and significant in order to support the scientific and sustainable management and protection of coastal marine resources. Artificial intelligence provides a valuable tool to improve marine resource monitoring. Deep learning methods have been widely used for marine object detection, but You Only Look Once (YOLO) models have not been employed for offshore aquaculture area monitoring. This study therefore evaluated the capacity of two well-known YOLO models, YOLOv5 and YOLOv7, to detect offshore aquaculture areas based on different high-resolution optical remote sensing imagery. Compared with YOLOv7 based on a satellite dataset, YOLOv5 increased the Precision value by approximately 3.29% (to 95.33%), Recall value by 3.02% (to 93.02%), mAP_0.5 by 2.03% (to 96.22%), and F1 score by 2.65% (to 94.16%). Based on the Google Earth dataset, YOLOv5 and YOLOv7 showed similar results. We found that the spatial resolution could affect the deep learning models’ performances. We used the Real-ESRGAN method to enhance the spatial resolution of satellite dataset and investigated whether super-resolution (SR) methods improved the detection accuracy of the YOLO models. The results indicated that despite improving the image clarity and resolution, the SR methods negatively affected the performance of the YOLO models for offshore aquaculture object detection. This suggests that attention should be paid to the use of SR methods before the application of deep learning models for object detection using remote sensing imagery.

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Section: Introductionmentioning

confidence: 99%

Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images

Dong,

Shi,

Hao

et al. 2024

JMSE

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Revolutionising agri‐energy: A comprehensive survey on the applications of artificial intelligence in agricultural energy internet

Fu,

Ye,

et al. 2024

Energy Internet

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Considering the rapid advancements in AI technologies such as reinforcement learning, ChatGPT, and deep learning, this paper conducts a comprehensive survey of the technological landscape of AI in the energy and agriculture sectors. It delineates the evolutionary path of AI technologies in smart grids and precision agriculture, highlighting significant advancements in energy prediction, optimisation of production and consumption, and intelligent management. Furthermore, the paper identifies key AI technologies crucial for the Agricultural Energy Internet (AEI), offering specialised exploration into AI applications for crop cultivation and fisheries, including disease detection, yield prediction, and resource management. The research provides essential theoretical foundations for AI integration in each of these agricultural domains. In addition, the paper envisions the future integration of ChatGPT in coupled modelling of agriculture and energy systems, enhancing synergistic intelligent control, and AI‐driven carbon tracking technologies within the AEI. This study facilitates a greater grasp of the transformative potential of AI in reshaping the nexus of agriculture and energy.

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Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments

Yu,

Han,

Gao

et al. 2024

JMSE

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Coastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced models: EfficientViT-NWD-YOLO v5 and Gold-NWD-YOLO v5. These models specifically target improvements to the backbone and neck structures of YOLO v5, respectively. The performance of these models for corrosion detection is analyzed in comparison with both YOLO v5 and NWD-YOLO v5. The evaluation metrics including precision, recall, F1-score, Frames Per Second (FPS), pre-processing time, inference time, non-maximum suppression time (NMS), and confusion matrix were used to evaluate the detection performance. The results indicate that the Gold-NWD-YOLO v5 model shows significant improvements in precision, recall, F1-score, and accurate prediction probability. However, it also increases inference time and NMS time, and decreases FPS. This suggests that while the modified neck structure significantly enhances detection performance in corrosion images, it also increases computational overhead. On the other hand, the EfficientViT-NWD-YOLO v5 model shows slight improvements in precision, recall, F1-score, and accurate prediction probability. Notably, it significantly reduces inference and NMS time, and greatly improves FPS. This indicates that modifications to the backbone structure do not notably enhance corrosion detection performance but significantly improve detection speed. From the application perspective, YOLO v5 and NWD-YOLO v5 are suitable for routine corrosion detection applications. Gold-NWD-YOLO v5 is better suited for scenarios requiring high precision in corrosion detection, while EfficientViT-NWD-YOLO v5 is ideal for applications needing a balance between speed and accuracy. The findings can guide decision making for corrosion health monitoring for critical infrastructure in coastal areas.

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Improved YOLOv5 Algorithm for Real-Time Prediction of Fish Yield in All Cage Schools

Cited by 3 publications

References 30 publications

Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images

Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images

Revolutionising agri‐energy: A comprehensive survey on the applications of artificial intelligence in agricultural energy internet

Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments

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