Channel Compression Optimization Oriented Bus Passenger Object Detection

Abstract EN

Bus passenger flow information can facilitate scientific dispatching plans, which is essential to decision making and operation performance evaluation.

Real-time acquisition of bus passenger flow information is an indispensable part for bus intellectualization.

The method of passenger flow statistics in bus video monitoring scene based on deep convolution neural network can provide rich information for passenger flow statistics.

In order to adapt to the real scenario of mobile and embedded devices on buses, and to consider the bandwidth limitation, this paper uses a lightweight network model M7, which is suitable for the vehicle system.

Based on the classic network model tiny YOLO, the model is optimized by a depthwise separable convolution method.

The optimized network model M7 reduces the number of parameters and improves the detection speed, while maintaining a low loss in detection accuracy.

As such, the network model M7 is compressed and further optimized by removing redundant channels.

The experimental results show that the detection speed of the network model target recognition after channel compression is 40%, which is faster than the precious channel compression on the premise of ensuring detection.