Triple Attention Mechanism with YOLOv5s for Fish Detection

Abstract

Traditional fish farming methods suffer from backward production, low efficiency, low yield, and environmental pollution. As a result of thorough research using deep learning technology, the industrial aquaculture model has experienced gradual maturation. A variety of complex factors makes it difficult to extract effective features, which results in less-than-good model performance. This paper proposes a fish detection method that combines a triple attention mechanism with a You Only Look Once (TAM-YOLO)model. In order to enhance the speed of model training, the process of data encapsulation incorporates positive sample matching. An exponential moving average (EMA) is incorporated into the training process to make the model more robust, and coordinate attention (CA) and a convolutional block attention module are integrated into the YOLOv5s backbone to enhance the feature extraction of channels and spatial locations. The extracted feature maps are input to the PANet path aggregation network, and the underlying information is stacked with the feature maps. The method improves the detection accuracy of underwater blurred and distorted fish images. Experimental results show that the proposed TAM-YOLO model outperforms YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, and SSD, with a mAP value of 95.88%, thus providing a new strategy for fish detection.

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“…Jubayer et al [22] found that the overall performance of YOLOv5 in detecting mould on food surfaces was superior to that of YOLOv4 and YOLOv3, achieving an average precision of 99.6%. Long et al [23] developed a system for fish detection, where YOLOv5 also obtained the highest mAP value of 95.95%, superior to YOLOv3 and YOLOv4. Ahmad et al [24] conducted a study comparing the performance of YOLO-Lite, YOLOv3, YOLOR, and YOLOv5 in identifying insect pests, with YOLOv5 emerging once more as the most successful, achieving an average precision of 98.3%.…”