YOLOv7-PE: A Precise and Efficient Enhancement of YOLOv7 for Underwater Target Detection

In underwater target detection tasks, challenges such as image blurring, complex backgrounds, and aggregation of small targets lead to problems such as difficulty in model feature extraction, target leakage, and false detection. In order to improve the accuracy, real-time performance and lightweight of underwater target detection models, we propose YOLOv7-PE: an accurate and efficient YOLOv7 improved model for underwater target detection.YOLOv7-PE is based on the single-stage target detection model YOLOv7 and separates the classification and regression tasks to be processed separately by decoupling the header design to enhance the feature extraction. We also introduce an anchor-free based design, which simplifies the target detection process, reduces the prediction time, and can adapt to targets in underwater environments. And to improve the computational efficiency, we introduce the CSPSPPF module, which reduces the computational cost of the model and improves the inference speed. In addition, we introduce the CBAM attention mechanism to enhance the feature representation in both channel and spatial dimensions. Through extensive qualitative and quantitative analyses, we verified that YOLOv7-PE has higher detection accuracy and efficient performance on the task of target detection in complex underwater environments. Relative to YOLOv7, the the average detection accuracy(mAP) of YOLOv7-PE is improved by 1.23%. Meanwhile, the Frames Per Second(FPS) of the model is improved by 1.52%, while the amount of model parameters is reduced by 6.78%. Our YOLOv7-PE model performs more accurately as well as efficiently compared to other classical target detection models.