Ms. Mandi Luo Profile

Mandi Luo

at Huazhong Univ of Science and Technology

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Proceedings Article | 8 July 2022 Paper

Deployment of road-garbage detection solution based on YOLOv5

Mandi Luo, Danhua Cao, Chenglong Lian, Longyin Hu, Cheng Hu

Proceedings Volume 12282, 122820J (2022) https://doi.org/10.1117/12.2611721

KEYWORDS: Automatic exposure, Cameras, Image processing, Imaging systems, Roads, Target detection, Control systems

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Because of changeable daylight and weather, the natural scenes of road are complicated. And the garbage on roads comes in polytropic shapes and sizes, especially small targets such as leaves. To detect the location and types of road-garbage in different scenarios quickly and accurately, we propose a road-garbage detection solution based on YOLOv5. The solution is finally applied to the garbage sweeper, where different deployment strategies are required for different modules. Firstly, to reduce the loss of image information caused by overexposure and underexposure, we design automatic exposure algorithm to adjust camera parameters in time and use OpenMP to accelerate it. Secondly, YOLOv5 algorithm based on object detection is trained for recognition and TensorRT framework is used for deployment. Taking into account the computing speed and accuracy, we choose FP16 computational precision for YOLOv5’s inference acceleration. Lastly, a low-cost computing platform named Jetson AGX Xavier is selected and the algorithm is optimized in combination with the characteristics of the hardware platform. Multithreading is also used to accelerate in software architecture. The results show that the average detection accuracy for various types of road-garbage reaches 70.4% under four different scenarios of self-built datasets. And the area of the smallest object detected accounts for 0.2‰ of the total area of the image. The speed of this road-garbage detection solution can reach 5fps when processing the 12-bit image of 2432*1226 size on the low-cost AGX Xavier computing platform.

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