Real-time vehicle detection under complex road conditions

Abstract

Vehicle detection is an important component in unmanned driving systems. This paper presents a real-time vehicle detection method under complex road conditions to solve the problem of real-time detection of road vehicles in automatic driving. Firstly using yolo network to build a deeper depth neural network, which is used to identify and score the vehicles in the image. Then, using the dynamic threshold method to remove some false candidate boxes, and using the Gauss attenuation function to filter the overlapping candidate boxes. The loss function normalized by the change rate is used to train the neural network, and the batch normalization layer is used to correct the input data of the network to avoid data deviation during the training process. Finally, a full convolution layer is added at the end of the network layer to transform the two-dimensional data into one-dimensional data and output the final recognition results. It is verified that this method improves the efficiency and accuracy of real-time vehicle detection, it can effectively detect vehicles on roads with complex backgrounds, and satisfy the real-time requirements of road vehicle detection.