Motorcyclist helmet detection in single images: a dual-detection framework with multi-head self-attention.

This paper focuses on the problem of motorcyclist helmet detection in single images. Although some previous works have been developed to deal with this problem, yet most of them are designed for videos and not suitable for single-image helmet detection. In view of this, in this paper, we propose a novel dual-detection framework for single-image motorcyclist helmet detection with multi-head self-attention. Particularly, two types of detectors are first trained, namely the rider detector and the head-shoulder detector, which are jointly leveraged in the dual-detection scheme. To take advantage of the contextual relevance information, the multi-head self-attention mechanism is incorporated, where multiple self-attention layers are integrated to capture the complex relationships among the input features so as to further enhance the detection accuracy. A new benchmark dataset, termed the SCAU helmet detection on motorcyclists (SCAU-HDM) dataset, is presented, which consists of 8000 training images and 2000 test images. Extensive experiments on the benchmark dataset demonstrate the effectiveness of the proposed framework. The code is available at https://github.com/LiChunHong2020/SCAUHDM. [ABSTRACT FROM AUTHOR]