Infrared small target detection algorithm deployed on HiSilicon Hi3531
In response to the existing shortcomings of large computational complexity,poor real-time performance,and deployment difficulties in current algorithms,and to meet the high requirements of real-time performance and accuracy for infrared detection systems,proposes a lightweight algorithm deployed on domestically produced embedded chips,termed YOLOv5-TinyHisi.The YOLOv5-TinyHisi algorithm undertakes lightweight modifications to the backbone network structure based on the characteristics of infrared small targets.Additionally,it utilizes SIoU optimized loss function for boundary error,thereby enhancing the accuracy of infrared small target localization.The YOLOv5-TinyHisi algorithm model is deployed on Hi3531DV200,utilizing the chip-integrated neural network inference engine(NNIE)to accelerate network inference.Experimental results on public datasets demonstrate that the algorithm achieves a 1.52%improvement in average precision(mAP)compared to YOLOv5,while significantly reducing parameter count and model size.On the Hi3531DV200,the inference speed for a single image with a resolution of(1 280×512)pixels reaches 35 frames per second(FPS),with a recall rate of 95%,meeting the real-time and accuracy requirements of the infrared detection system.
infrared small target detectionembedded systemYOLOv5neural network inference engine
万方数据
维普
