Object detection

Real-time computing

Set (abstract data type)

Computer science

False positive rate

Pattern recognition (psychology)

Data set

Artificial intelligence

Programming language

Computer vision

Sustainability

To reduce the false detection rate of vehicle targets caused by occlusion, an improved method of vehicle detection in different traffic scenarios based on an improved YOLO v5 network is proposed. The proposed method uses the Flip-Mosaic algorithm to enhance the network’s perception of small targets. A multi-type vehicle target dataset collected in different scenarios was set up. The detection model was trained based on the dataset. The experimental results showed that the Flip-Mosaic data enhancement algorithm can improve the accuracy of vehicle detection and reduce the false detection rate.

Real-Time Vehicle Detection Based on Improved YOLO v5