Citation

Luo WY, Fu MY. J. Graphics 2023; 44(3): 465-472.

Copyright

(Copyright © 2023, China Association for Science and Technology)

DOI

10.11996/JG.j.2095-302X.2023030465

PMID

unavailable

Abstract

Every year in China, a large number of people die from drowning due to illegal swimming and fishing in reservoirs, rivers, and lakes. These bodies of water are often located in remote areas, making it difficult for staff to supervise them 24 hours a day. Existing target detection methods are either too slow or inaccurate, too large to deploy, or incapable of detecting illegal swimming and fishing in real-time. To address these shortcomings, we proposed the YoloX-ECA model with an attention model. By adding the efficient channel attention (ECA) block to the CSPLayer and FPN, we aimed to improve detection performance for swimming and fishing while maintaining detection speed. Experimental results on self-made datasets showed that the YoloX-ECA achieved over 90% AP for the detection of swimming and fishing classes, with a detection speed of 62.29 fps. Compared with YoloX, mAP was increased by 1.21%. Furthermore, YoloX-ECA's performance also outperformed other target detection algorithms such as Faster-RCNN. The improved YoloX-ECA model achieved the expected design goals and displayed great prospects for application in the field of intelligent supervision of rivers and lakes.

Language: zh

Keywords

object detection; attention model; ECA; river and lake supervision; YoloX