Comparison of object detection models for automatic traffic surveys

Abstract

Traffic surveys are carried by manual. For automation, traffic counting application of vehicle types using object detection technology has been developed. In previous studies, SSD300 with VGG16 as the base network was adopted, however assuming actual operation, the required speed for data processing and accuracy differ depending on the survey condition, and thus it is desirable to use the model properly according to the situation. This study compared the performance (the inference time and mAP) of learning the same data set with SSD models with three networks (VGG, MobileNetV1 and MobileNetV2). MobileNetV1 and V2 won the VGG with similar mAP, however V1 and V2 had similar inference time.