Overcoming the Challenges of Long-Tail Distribution in Nighttime Vehicle Detection

As the basic task of an intelligent transportation system, nighttime vehicle detection is associated with many challenges. Existing methods usually ignore the significant challenges that arise from imbalanced class distribution among vehicles, which always leads to poor detection for vehicles that belong to tail classes. By analyzing the existing solutions for long-tail object detection and considering the complex and diverse characteristics of nighttime traffic scenarios, we propose an enhanced detection approach based on anomaly detection. In addition, to tackle disturbance from complex lights, we reconstruct the loss function for background proposals, thus allowing the detector to pay more attention to hard-classified proposals and learn to distinguish vehicle lights from disturbed light resources. Comprehensive experiments prove that, compared with generic approaches, our proposed method can effectively solve the problem of long-tail distribution in nighttime vehicle detection and improve robustness in complex environments. © 2024 The Authors.