Objective Infrared image technology is capable of working in low-light and adverse weather conditions. Infrared vehicle detection technology is designed to use infrared sensors to monitor vehicles on roads, enabling the collection and analysis of information related to vehicle quantity and speed, which can be used to achieve traffic management and safety control. This technology can be applied not only to road vehicles, but also to rail transport, airports, and ports, providing effective technical support for the safety and convenience of the transportation industries. However, infrared vehicle detection still faces many challenges due to the low resolution, low contrast, and blurred edges of small targets in infrared images. Traditional hand-crafted image feature extraction methods are not adaptable nor robust, require substantial prior knowledge and have low efficiency. Therefore, this paper aims to explore deep learning-based vehicle detection models, which plays an important role in traffic regulation.
Methods YOLOv5 is a one-stage object detection algorithm that is characterized by its lightweight design, ease of deployment, and high accuracy, making it widely used in industrial applications. In this paper, a CFG mixed attention mechanism (Fig.2) is introduced into the model backbone to help the model better locate the vehicle area in the image and improve its feature extraction ability, due to the low resolution of infrared images. In the feature fusion part, an improved Z-BiFPN structure (Fig.5) is proposed to incorporate more information in the shallow fusion, thereby improving the utilization of shallow information. A small object detection layer is added, and the Decoupled Head (Fig.6) is used to separate classification and regression, improving the model's ability to detect small target vehicles.
Results and Discussions In order to improve the model's generalization ability, an infrared image dataset INFrared-417 (Fig.7) consisting of seven categories of bus, truck, car, van, person, bicycle and elecmot, was constructed by collecting data and combining existing infrared datasets. The main evaluation metrics used were AP (Average Precision) and mAP (mean Average Precision), with P (Precision) and R (Recall) as secondary metrics for the experiments. The ablation experiment results (Tab.1) confirmed the effectiveness and feasibility of the proposed improvement methods, with mAP improving by 4.0%, and AP significantly improving for the van, person, and bicycle categories, while P increased by 1.7% and R increased by 3.6%. In addition, the comparison results (Fig.10) demonstrated that the improved model reduced false alarm and missed detection rates, while improving the detection of small targets. The comparison experiment results (Tab.2) also showed that the proposed improved model had excellent performance in terms of detection accuracy and model parameter count.
Conclusions This paper proposes an improved infrared vehicle detection algorithm. By introducing the mixed attention mechanism, the model is able to better focus on the vehicle region in the image and enhance its feature extraction ability. The improved Z-BiFPN is used in the model neck to efficiently integrate context information. At the same time, the detection head is replaced with a more advanced Decoupled Head to improve the detection ability, and a small object detection layer is added to improve the ability to capture small targets. It is hoped that this model can be applied in traffic control.
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