Real-Time Detection Algorithm of Marine Organisms Based on Improved YOLOv4-Tiny

Abstract

Marine organisms detection based on machine vision requires high real-time performance and accuracy. Network feature extraction is frequently made more difficult when underwater robots collect information on the seafloor because of the uneven distribution of light on the seafloor, the significant impact of water waves, and the complexity of the seafloor environment. How to detect marine organisms quickly and accurately is a great difficulty and challenge. To address the above problems, we propose a MODA (Marine Organism Detection Algorithm) based on an improved YOLOv4-tiny marine organism detection algorithm. Firstly, the Coordinate Attention module, an ultra-lightweight attention mechanism, is constructed and embedded into the backbone network to retain more information about the target of interest and enhance the feature extraction capability of the network. Secondly, the Hybrid Dilated Convolution (HDC) structure is constructed and added to the improved network to expand the feature map perception field and obtain richer semantic information to improve the network detection accuracy. Finally, a better MODA model is proposed based on the two methods mentioned above. The experimental results show that the improved model MODA improves the mAP metric from 74% to 76.62% on the URPC dataset and only increases the computational effort by 0.06 GM compared with the original YOLOv4-tiny model; the mAP metric improves from 92.37% to 98.41% on the Aquarium dataset. This improvement indicates that the MODA model is more suitable for marine organisms detection tasks.