MobileNetV3-CenterNet: A Target Recognition Method for Avoiding Missed Detection Effectively Based on a Lightweight Network

Abstract

To solve the problems in online target detection on the embedded platform, such as high missed detection rate, low accuracy, and slow speed, a lightweight target recognition method of MobileNetV3-CenterNet is proposed. This method combines the anchor-free Centernet network with the MobileNetV3 small network and is trained on the University at Albany Detection and Tracking (UA-DETRAC) and the Pattern Analysis, Statical Modeling and Computational Learning Visual Object Classes(PASCAL VOC) 07+12 standard datasets. While reducing the scale of the network model, the MobileNetV3-CenterNet model shows a good balance in the accuracy and speed of target recognition and effectively solves the problems of missing detection of dense and small targets in online detection. To verify the recognition performance of the model, it is tested on 2683 images of the UA-DETRAC and PASCAL VOC 07+12 datasets, and compared with the analysis results of CenterNet-Deep Layer Aggregation (DLA) 34, CenterNet-Residual Network (ResNet) 18, CenterNet-MobileNetV3-large, You Only Look Once vision 3(YOLOv3), MobileNetV2-YOLOv3, Single Shot Multibox Detector (SSD), MobileNetV2-SSD and Faster region convolutional neural network (RCNN) models. The results show that the MobileNetV3-CenterNet model accurately recognized the dense targets and small targets missed by other methods, and obtained a recognition accuracy of 99.4% with a running speed at 53 (on a server) and 14 (on an ipad) frame/s, respectively. The MobileNetV3-CenterNet lightweight target recognition method will provide effective technical support for the target recognition of deep learning networks in embedded platforms and online detection.

Keywords

target detection / MobileNetV3 / CenterNet / lightweight