Small-Target Pedestrian-Detection Algorithm Based on Improved YOLOv4

Pedestrian detection is vital to applications in unmanned environment perception.Most existing pedestrian-detection algorithms focus only on ordinary pedestrian targets and do not consider the low accuracy caused by the insufficient pedestrian feature information of small targets;furthermore, they do not offer favorable real-time performance when applied to embedded devices.Hence, a small-target pedestrian-detection algorithm, YOLOv4-DBF, is proposed herein.The conventional convolution is replaced with deeply separable convolution in the YOLOv4 algorithm, which reduces the number of parameters and the computation time of the model, as well as improves the detection speed and real-time performance of the algorithm.Additionally, the concurrent spatial and channel Squeeze & Excitation(scSE) attention module is introduced into the feature fusion component of the YOLOv4 backbone network to enhance the important channels and spatial features of the input pedestrian feature map as well as to enable the network to learn more meaningful feature information.The feature fusion component of the Feature Pyramid Network(FPN) in the YOLOv4 neck is improved to enhance the multiscale feature learning of the pedestrian target in the image, which improves the detection accuracy but increases the amount of computation.After training and verification based on the VOC07+12+COCO dataset, the results show that compared with the original YOLOv4 algorithm, YOLOv4-DBF increases the Average Precision(AP) by 4.16 percentage points and the speed by 27%.Finally, YOLOv4-DBF is accelerate deployed on the TX2 equipment of an unmanned vehicle for real-time testing, where the maximum speed reaches 23FPS.The algorithm proposed herein can effectively improve the accuracy and real-time performance of small-target pedestrian detection.