Road Target Detection in Different Weather Conditions Based on Deep Learning

Abstract

Addressing the challenge of ensuring robustness in vision-based target recognition algorithms under adverse weather conditions, such as rain, snow, and fog, is crucial. In this paper, we introduce a novel approach for road target detection that can effectively operate under various weather conditions. Our method is based on the cascade task framework of target detection, complemented by image restoration techniques. Specifically, we have developed a denoising network tailored to meet the demands of de-raining and snow removal tasks. This network leverages prior knowledge about the mask, enhancing its effectiveness. In real-world scenarios featuring fog, wet conditions, and snow-covered roads, our proposed method demonstrates a significant improvement in both recall rate and accuracy compared to conventional single-object detection algorithms. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.