第 6 期 王鑫怡等:基于改进 YOLOv8 的轻量级鱼类检测方法
Lightweight fish detection method based on improved YOLOv8
WANG Xinyi
1,5
,LIU Xuteng
2
,ZHENG Jiye
1,5
,DONG Guancang
3
,YU Zhaohui
4
,
ZHANG Xia
5
,WANG Xingjia
1,5
(1Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences,
Jinan 250100, Shandong,China;
2 The Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, Guangdong,China;
3 Shandong Freshwater Fisheries Research Institute, Jinan 250013, Shandong,China;
4 Shandong Dongrun Instrument Science and Technology Co, Ltd, Yantai 264003, Shandong, China;
5 School of Physical Science and Information Engineering, Liaocheng University,
Liaocheng 252000, Shandong,China)
Abstract:Fish culture is moving toward precision culture, and fish target detection is an important part of
precision culture. Fortunately, the use of deep learning holds promise for fish target detection. However, the
existing fish target detection models have problems with heavy computation and low accuracy. To address the
issues of low accuracy and high computational load in fish target detection,a lightweight fish target detection
method based on an improved YOLOv8 model was proposed and named YOLOv8-FCW in this study. Firstly,
The experimental comparison of MobileNet, ShuffleNet, GhostNet and C2f-Faster shows that C2f-Faster
performs best. Therefore,the FasterBlock from FasterNet was introduced to replace the Bottleneck module in
C2f of YOLOv8, reducing redundant computations in the network model. Secondly, the Convolutional Block
Attention Module (CBAM) attention mechanism was incorporated to efficiently extract fish body features and
enhance the detection accuracy of the network model. Finally, The experimental results show that the loss
value and convergence speed of the Wise intersection over union (WIoU) loss function is better than Complete
intersection over union (CIoU), Distance intersection over union (DIoU) and Generalized intersection over
union ( GIoU). Therefore, a dynamic non-monotonic focusing mechanism WIoU was introduced to replace
CIoU, accelerating the convergence speed of the network model and improving its detection performance. To
verify the detection effect of YOLOv8-FCW on fish, the original model and YOLOv8-FCW were trained and
tested on the fish data set. The fish data set consists of 1000 images, which were divided into training set,
verification set and test set according to the ratio of 8 ∶ 1 ∶ 1. Experimental results show that compared with
the original model, the improved YOLOv8-FCW model had increased precision by 1. 6 percentage points,
recall by 5. 1 percentage points, and mean average precision( mAP) by 2. 4 percentage points, while the
weight and computational load were reduced to 80% and 79% of the original model, respectively. YOLOv8-
FCW achieves high detection accuracy and efficiency with very small model volume and low computational
cost. The model shows high accuracy and robustness. The research can help breeders accurately calculate the
number of fish and provide technical references for fish target detection.
Key words:image processing; image recognition; target detection; YOLOv8
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