A Survey of Object Detection Methods in Inclement Weather Conditions

In recent years, significant progress has been achieved in object detection based on Deep Neural Netwroks (DNN). However, the object detecting in inclement weather conditions remains a challenging problem, as the performance of certain sensor degrades. In unfavorable weather conditions such as haze, rainy day and snow day, the sensor information obtained from the environment becomes more ambiguous, presenting numerous difficulties for subsequent object detection tasks. This survey provides a comprehensive overview of the recent advances in the field of computer vision regarding object detection tasks in inclement weather conditions. The article begins by describing, comparing, and analyzing existing relevant databases. It then reviews image-based object detection algorithms specifically de-signed to address the challenges posed by inclement weather conditions. Finally, it discusses the methods and applications of multi-modal data-based object detection approaches in inclement weather conditions. The article also discusses future works and potential areas of research in this field.