WMF-YOLOv7: A Water Surface Object Detection Algorithm Based on Radar Camera Fusion Improved YOLOv7

In recent years, many deep learning-based water surface object detection algorithms have been well applied in large-scale object recognition. However, in complex water surface environments, the previous algorithms can not achieve good performance due to the influence of darkness, rain and fog. To address the above problem, we propose a water surface object detection model, named WMF-YOLOv7, which is based on YOLOv7 with three modifications. Firstly, a novel radar image conversion method is proposed to extract effective features from the sparse points cloud of millimeter-wave radar; secondly, a deformable convolution-based radar feature extraction method is used to reduce the nuisance alarm rate of feature fusion caused by the misalignment of optical image and radar image; finally, a data fusion module based on the normalised attention mechanism is used to reduce the effect of noise in radar images effectively through the sparse weight penalty mechanism. The experimental results show that the proposed method outperforms the benchmark algorithm in terms of recognition accuracy and robustness.