ImprovedRain removal network clarity and object detection performance in heavy rain images using complementary structure

In heavy rain situations, the clarity of both human vision and computer vision is significantly reduced. Rain removal GAN-based networks have been proposed as a means of resolving this problem. However, such methods have only a limited effectiveness in improving the object detection accuracy. Accordingly, this study commences by analyzing the object detection performance before and after rain removal, respectively. We propose an integrated framework for improving the object detection performance in heavy rain images based on the analysis results. The experimental results show that the proposed framework yields an improved IoU and reduces the error rate compared with existing methods.