Real-time human detection in thermal infrared imaging at night using enhanced Tiny-yolov3 network

Autor: Shaoyuan Sun, Samah A. F. Manssor, Mohammed Abdalmajed, Shima Ali
Rok vydání: 2021
Předmět:
Zdroj: Journal of Real-Time Image Processing. 19:261-274
ISSN: 1861-8219
1861-8200
DOI: 10.1007/s11554-021-01182-z
Popis: Human detection is a technology that detects human shapes in the image and ignores everything else. However, modern person detectors have some inefficiencies in detecting pedestrians during video surveillance at night, and the accuracy rate is still insufficient. Therefore, this paper aims to increase the accuracy rate for automatic human detection at night from thermal infrared (TIR) images and real-time video sequences. For this purpose, a new architecture is proposed to enhance the backbone of the Tiny-yolov3 network. The enhanced network used the YOLOv3 algorithm’s tasks with the K-means clustering method to extract more complex features of a person. This network was pre-trained on the MS. COCO dataset to obtain the initial weights. Through the comparison with other related methods showed that the experimental results have achieved the significantly improved performance of human detection from thermal imaging in terms of accuracy, speed, and detection time. The method has achieved a high accuracy rate (90%) compared with the TF-YOLOv3 (88%) trained on the DHU Night Dataset. Although the method has achieved an accuracy rate equal to the YOLOv3-Human (90%), the detection time (4.88 ms) is less, Furthermore, the method has a higher accuracy rate (49.8%) than the YOLO (29.36%) and TF-YOLOv3 (29.8%) with lower detection time (8 ms) on the FLIR Dataset. In addition, the model has achieved a good TP detection for multiple small size of person. By improving the performance of human detection in thermal imaging at night, the method will be able to detect intruders in the night surveillance system.
Databáze: OpenAIRE
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