An Improved Tuna-YOLO Model Based on YOLO v3 for Real-Time Tuna Detection Considering Lightweight Deployment

Abstract

A real-time tuna detection network on mobile devices is a common tool for accurate tuna catch statistics. However, most object detection models have multiple parameters, and normal mobile devices have difficulties in satisfying real-time detection. Based on YOLOv3, this paper proposes a Tuna-YOLO, which is a lightweight object detection network for mobile devices. Firstly, following a comparison of the performance of various lightweight backbone networks, the MobileNet v3 was used as a backbone structure to reduce the number of parameters and calculations. Secondly, the SENET module was replaced with a CBAM attention module to further improve the feature extraction ability of tuna. Then, the knowledge distillation was used to make the Tuna-YOLO detect more accurate. We created a small dataset by deframing electronic surveillance video of fishing boats and labeled the data. After data annotation on the dataset, the K-means algorithm was used to get nine better anchor boxes on the basis of label information, which was used to improve the detection precision. In addition, we compared the detection performance of the Tuna-YOLO and three versions of YOLO v5-6.1 s/m/l after image enhancement. The results show that the Tuna-YOLO reduces the parameters of YOLOv3 from 234.74 MB to 88.45 MB, increases detection precision from 93.33% to 95.83%, and increases the calculation speed from 10.12 fps to 15.23 fps. The performance of the Tuna-YOLO is better than three versions of YOLO v5-6.1 s/m/l. Tuna-YOLO provides a basis for subsequent deployment of algorithms to mobile devices and real-time catch statistics.

Keywords

• tuna detection
• catch statistics
• lightweight network
• attention mechanisms
• knowledge distillation
• image augmentation