Research on an improved fish recognition algorithm based on YOLOX

Abstract

The key to the development of underwater resources is to detect underwater targets quickly and accurately in real time. However, due to the influence of light, the underwater image is easy to be distorted and the contrast is low and so on, which greatly affects the performance of the detection algorithm, In order to improve the detection accuracy of underwater targets, After a detailed analysis of the underwater detection target features, The attention mechanism ECA module was added to the YOLOX model, Real-ESRGAN was used to treat multiple target and fuzzy images in detection images, the accuracy improved about 10 percent, A high-precision target detection algorithm suitable for underwater fish was developed, The ideal detection result was achieved.

Keywords

• deep learning
• underwater target detection
• yolox
• attention mechanism eca
• esrgan