Vehicle detection method with low-carbon technology in haze weather based on deep neural network

Vehicle detection based on deep learning achieves excellent results in normal environments, but it is still challenging to detect objects in low-quality picture obtained in hazy weather. Existing methods tend to ignore favorable latent information and it is difficult to balance speed and accuracy, etc. Therefore, the existing deep neural network is studied, and the YOLOv3 algorithm is improved based on ResNet. Aiming at the problem of low utilization of shallow features, DensNet is added in the feature extraction stage to reduce feature loss and increase utilization. An attention module is added in the feature extraction and fusion stage to better focus on potential information and improve the detection accuracy in haze weather. In view of the difficulty of vehicle detection in haze weather, focal loss is introduced to give more weights to difficult samples, balance the number of difficult and easy samples and improve detection accuracy. The experimental results show that the recognition accuracy of the improved network for vehicles reaches 75%, which proves the effectiveness of the method.