Developing a Dead Fish Recognition Model Based on an Improved YOLOv5s Model - Chengbiao Tong; Biyu Li; Jiting Wu; Xinming Xu (Applied Sciences)
To address the challenges of low detection accuracy and high difficulty in dead fish identification due to water surface reflections, low contrast between targets and the environment, uncertain positions between detection sources and targets, and the impact of water mist blurriness, this paper proposes a dead fish recognition model named YOLO-DWM based on an improved YOLOv5s model. To address the weak feature extraction capabilities of existing convolutional modules for targets, a multi-scale convolutional module (DWMConv) based on depthwise separable convolution is designed, enhancing the detection performance for targets. To further improve accuracy, the EMA mechanism is embedded in the C3 module, enhancing its feature processing capabilities. Additionally, to reduce the model’s parameters and FLOPs, a lightweight approach called C3-Light is introduced, which replaces the Conv convolution in the C3 module with a DWConv convolution. A total of 670 images of dead fish in a fish farm were collected prior to the experiment and used to train the model. The experimental results demonstrate that, compared with Faster RCNN, SSD, YOLOV3-tiny, YOLOv5s, YOLOv6n, and YOLOv8n, the YOLO-DWM model exhibits superior performance, with mAP increases of 8.13%, 12.3%, 5.9%, 4.5%, 4.5%, and 2.8% and increases in the F1 score of 17.5%, 7.5%, 2.7%, 2.6%, 3.0%, and 0.4%.