Real-time pedestrian detection method based on improved YOLOv3

Abstract

Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from VOC data set and train with INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2 and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Detection of Pedestrians in Reverse Camera Using Multimodal Convolutional Neural Networks

“…The architecture generated more effective ROIs for small-scale pedestrian detection, achieving an AP of 90.51%, slightly outperforming architectures such as YOLOv3 and YOLOv2 with 89.77% and 71.53%, respectively. Meanwhile, Luo et al [28] proposed RT-YOLOv3, an improved version of YOLOv3, for real-time forward-pedestrian detection at different scales. The proposed method achieved a precision of 93.57% mAP and 46.52 f/s , meeting the requirements for real-time pedestrian detection.…”

Machine Recognition of Map Point Symbols Based on YOLOv3 and Automatic Configuration Associated with POI

“…Point of interest (hereinafter referred to as POI) objects such as schools, hospitals, and shopping malls contain abundant human activity characteristics, which reduce symbol recognition errors caused by background information interference. The You Only Look Once Version 3 (hereinafter referred to as YOLOv3) [23,24] algorithm has the advantage of recognizing small targets [25,26] with respect to both real-time performance and accuracy. An attention module can be added to enhance the symbol feature extraction capability for problems such as symbol deformation during map scanning.…”