Critical review of object detection techniques for traffic light detection in intelligent transportation systems

Object detection and tracking play a critical role in intelligent transportation systems (ITS), particularly in recognizing and monitoring traffic lights to ensure safety and improve traffic efficiency. Despite progress in deep learning and optimization algorithms, traffic light detection still faces persistent challenges under varying conditions such as illumination changes, occlusions, and visual clutter. This study provides a critical review of object detection techniques specifically for traffic light detection, evaluating the evolution of machine learning frameworks, deep learning architectures, and hybrid optimization models. The review identifies research gaps in the robustness, real-time adaptability, and generalizability of existing methods. Furthermore, it highlights emerging trends such as multi-camera systems, anchor-free detection, and hybrid optimization techniques that bridge performance trade-offs between accuracy and efficiency. The findings offer a new perspective on integrating multiple approaches to achieve scalable, high-accuracy traffic light detection for future ITS applications.