Improved mine pedestrian detection algorithm based on YOLOv4-Tiny
Fengbo Wu, Wei Liu, Shuqi Wang, Gang Zhang · Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022) · 2023
In view of the complex underground structure and harsh environment of coal mines, it is easy to cause problems such as low pedestrian detection accuracy and missed detection. An improved mine pedestrian detection algorithm based on YOLOv4-Tiny was proposed. The algorithm introduced spatial pyramid pooling (SPP) module after 13×13 features, which realized the extraction of global and local features of image information, and finally improved the detection precision of the system. The mAP of the improved algorithm was 91.98%, which was 2.32% higher than the original algorithm, the precision increased by 3.87% and the recall by 0.93%. The experimental results showed that the algorithm improved the detection of the mine pedestrian system effectively and had a better detection effect.