Traffic light detection is a challenging problem in the context of self-driving cars and driver assistance systems. While most existing systems produce good results on large traffic lights, detecting small and tiny ones is often overlooked. A key problem here is the inherent downsampling in CNNs, leading to low-resolution features for detection. To mitigate this problem, we propose a new traffic light detection system, comprising a novel traffic light proposal generator that utilizes findings from general object proposal generation, fine-grained multi-scale features, and attention for efficient processing. Moreover, we design a new detection head for classifying and refining our proposals. We evaluate our system on three challenging, publicly available datasets and compare it against six methods. The results show substantial improvements of at least $12.6\%$ on small and tiny traffic lights, as well as strong results across all sizes of traffic lights.

翻译：交通灯检测是自动驾驶汽车和驾驶员辅助系统领域中的一个挑战性问题。尽管现有大多数系统在大型交通灯上表现良好，但检测小型和微型交通灯通常被忽视。其中的关键问题在于卷积神经网络固有的降采样特性，导致用于检测的特征分辨率较低。为解决这一问题，我们提出了一种新型交通灯检测系统，该系统包含一个创新的交通灯提案生成器，结合了通用目标提案生成、细粒度多尺度特征和注意力机制以实现高效处理。此外，我们设计了一个新的检测头用于分类和优化提案。我们在三个具有挑战性的公开数据集上评估了该系统，并将其与六种方法进行了比较。结果表明，在小型和微型交通灯上，性能至少提升了$12.6\%$，且在所有尺寸的交通灯上均取得了优异结果。