Abstract
Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.
Highlights
Target detection [1] is an important research direction in the field of computer vision
With the rapid development of deep learning, new target detection algorithms continue to emerge in the visible light environment, but related algorithms rely heavily on sufficient lighting conditions and cannot meet the target detection requirements in under-lighted scenes
Infrared thermal imaging refers to the use of the reflection of infrared light and the thermal radiation signal of the target to convert it into an image that human vision can accept and perceive. It can image the surrounding environment under conditions such as darkness and strong light, can cover most of the lack of light, and can cover most scenes with insufficient light to achieve all-weather and all-time detection. At this stage, infrared pedestrian detection algorithms can be roughly divided into two types
Summary
Target detection [1] is an important research direction in the field of computer vision. Infrared thermal imaging refers to the use of the reflection of infrared light and the thermal radiation signal of the target to convert it into an image that human vision can accept and perceive. It can image the surrounding environment under conditions such as darkness and strong light, can cover most of the lack of light, and can cover most scenes with insufficient light to achieve all-weather and all-time detection. At this stage, infrared pedestrian detection algorithms can be roughly divided into two types. One is based on the artificially designed pedestrian template ratio. e target contour is extracted by the artificially designed target contour extraction method and compared with the template
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