{'fraction_non_alphanumeric': 0.02195121951219512, 'fraction_numerical': 0.005691056910569106, 'mean_word_length': 5.280612244897959, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Benchmark Dataset and Multi-Scale Attention Network for Semantic Traffic Light Detection

Accurate traffic light detection is an important problem for intelligent vehicles. While most existing methods deal with classification of red, green and yellow signals, traffic lights in real world contain a diversity of specific semantic forms, such as red right-turn arrow, green left-turn arrow, and red U turn. In this paper, we propose a multi-scale attention (MSA) network for semantic traffic light detection. The task is to infer the bounding boxes of traffic lights in images and identify their specific semantic categories. Our MSA network combines multi-scale information with attention blocks overcome the ‘small object’ challenge and increase the computation efficiency. Since there was no a proper dataset containing specific semantic traffic lights, we collected a large scale semantic traffic light dataset. Our dataset contains 11 standard categories of specific semantic traffic lights and about 14800 sample images. We test the proposed approach on the new dataset, Bosch Small Traffic Lights Dataset, and LISA Dataset. Experiments show that the proposed method improves the performance of the traffic light detection.