Systematic evaluation of deep learning-based vehicle detection methods

Vehicle detection is a fundamental task in computer vision, crucial for applications like traffic management and autonomous driving. However, real-world conditions, particularly low-light environments, pose significant challenges. This paper presents a systematic evaluation of vehicle detection performance under different training strategies, using the state-of-the-art models. We compare joint training (daytime and nighttime images) with separate training (daytime or nighttime only) and investigate the impact of Low-Light Image Enhancement (LLIE). Our findings reveal a model-dependent optimal training strategy and the effect of LLIE on detection accuracy. These results highlight the complex interplay between training data, model architecture, and image enhancement, underscoring the need for careful consideration when deploying robust vehicle detection systems in challenging conditions. This research provides crucial guidelines for optimizing training and challenges common assumptions about the relationship between visual quality and detection performance.