Semantic segmentation of nocturnal infrared images of unmanned vehicles based on improved DeepLabv3+

In order to enhance the understanding ability of unmanned vehicle to night scene, a semantic segmentation algorithm based on improved DeepLabv3+ network is proposed for infrared images acquired by unmanned vehicles at night. Because the objects in the autopilot scene often show very large scale changes, the algorithm based on DeepLabv3+ network can cover a larger scale range by introducing densely connected atrous spatial pyramid pooling module. In addition, the algorithm splices the multi-layer results of the encoder module into the decoder module to recover more spatial information and low-level features lost in the downsampling process. Through end-to-end learning and training, it can be directly used for semantics segmentation of night vision infrared images. The experimental results show that the segmentation accuracy of the algorithm is better than that of the original DeepLabv3+ algorithm, and the mean intersection over union reaches 80.42, which has good real-time performance and accuracy.