Object Detection in Transitional Weather Conditions for Autonomous Vehicles

Navigating safely and dependably through challenging weather conditions poses a significant hurdle for autonomous vehicles (AVs). While state-of-the-art object detection models have demonstrated superior performance on standard benchmark datasets, their accuracy is compromised by visual variations introduced by adverse weather conditions. In addition, we naturally observe the continuous shifts between discrete weather conditions (cloudy to rainy, rainy to sunny, etc.), with variation in different levels of adversity. The current object detection research predominantly concentrates on identifying objects in discrete weather conditions (extremely cloudy, rainy, etc.). However, there is a lack of emphasis on continuous shifts between these stationary weather conditions. In response to this challenge, we introduce a pioneering solution, the Multi-Scale Adaptive Transformer (mSAT). This innovative approach amalgamates a Domain Adaptive Network (DAN), adept at identifying continuous weather-invariant features across various scales, with a transformer network tailored for object detection. Our method is evaluated on the AIWD6 dataset, showcasing its efficacy in addressing the impact of adverse weather conditions on object detection. Our approach effectively mitigates the domain discrepancy, enabling adaptation to various continuous weather shifts. Later, we introduce three novel metrics for evaluating object detection performance on continuous weather data along with standard metrics. Our proposed mSAT, designed to operate on various intensity levels of weather with unlabeled target data, achieves 74.6 mAP on the AIWD6 dataset. Experimental results demonstrate that our model adapts to continuous weather shifts and effectively performs object detection.