Abstract
The principle of infrared image is thermal imaging technology. Infrared pedestrian detection technology can be applied to the safety monitoring of the elderly, which can not only protect personal privacy, but also realize pedestrian identification at night, which has strong application value and social significance. A method of infrared image pedestrian detection with improved YOLOv3 algorithm is proposed to increase the detection accuracy and solve the problem of low detection accuracy caused by infrared pedestrian target edge blurring. And according to the characteristics of infrared pedestrian, a complex sample data set is established which is applied to infrared pedestrian detection. The infrared image enhancement method with WDSR-B is adopted to improve the clarity of the data set. In addition, based on YOLOv3 algorithm, the output of the 4-time down-sampling layer is added to obtain richer context information for small targets and improve the detection performance of the network for small-target pedestrians. And the improved YOLOv3 network is trained by the enhanced infrared data set. Experimental results show that the scheme precision of pedestrian detection is higher than that of YOLOv3 algorithm. Therefore, this method can be applied to the detection of pedestrians at night and the safety monitoring of the elderly.
Highlights
The principle of infrared imaging is thermal imaging technology, which can identify pedestrians in a timely manner under unusual environments such as nights and cloudy days
Experimental results show that WDSR-B network performs best among the four super resolution algorithms, and the conclusion is applicable to infrared image
The area of the graph formed by the accuracy-recall curve and the coordinate axis is the result of average precision
Summary
The principle of infrared imaging is thermal imaging technology, which can identify pedestrians in a timely manner under unusual environments such as nights and cloudy days. There are some problems such as fuzzy edges and indistinct features, which limit the accuracy of infrared image pedestrian detection [16]. Aiming at the problem that the low resolution of the target affects the detection accuracy, scholars have proposed some methods. The super-resolution algorithm is applied to Fast R CNN network to improve the detection performance of low-resolution targets [1], the positive effect of super-resolution is analyzed on the target detection performance of satellite images [2]. The effect of super-resolution algorithm on the performance of the infrared pedestrian target detection algorithm has yet to be verified. We use WDSR-B network to enhance the infrared image and verify the influence of the super-resolution algorithm on the performance of the infrared pedestrian target detection algorithm
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