Target Detection and Image Enhancement for Underwater Environment: Research on Improving YOLOv7 - Yang Luo; Wen Feng (IEEE Access)
Aiming at the common low accuracy and efficiency problems in underwater target detection, this paper designs an innovative algorithm based on the YOLOv7 framework. The core of the research lies in enhancing the feature extraction capability of the model, optimizing the feature expression of the model by introducing the CBAM attention mechanism, and improving the original structure of the neck component. To further enhance the computational efficiency, a deep decomposition feature expression module is designed, which significantly reduces the computational complexity and the number of parameters of the model. In addition, by introducing the Focal-EIoU loss function, combined with a more accurate penalty mechanism, the matching between the predicted frame and the actual labelled frame is made more accurate, which effectively improves the accuracy and reliability of detection. Meanwhile, the image enhancement algorithm designed in this paper plays an important role in improving the quality of the input data, which effectively enhances the performance of the target detection model in the complex underwater environment. Experimental results show that on the URPC2020 dataset, the method in this paper improves mAP@0.5 by 2% compared to YOLOv7 while reducing the computational complexity by 14.4%.