YOLOv3 tiny vehicle: a new model for real-time vehicle detection

Existing vehicle detection has the problem of unbalanced detection accuracy and speed. Aiming at this problem, this paper proposes a new real-time vehicle detection model named YOLOv3 Tiny Vehicle. The proposed network replaces the Maxpooling layers of the original network with the convolutional layers to ensure that the characteristic information of the vehicle was preserved to the greatest extent. On this basis, our work adds a dense connection structure to the original network, which greatly reduces or even eliminates the overfitting problem during network training. The experimental results show that the mean Average Precision (mAP) of the model on the Beijing Institute of Technology vehicle (BIT-Vehicle) dataset can reach 96.80%, the Frames per second (FPS) can reach 188. At the same time, it also shows that our model has preeminent generalization ability.