Real-time semantic segmentation of farmland at night using infrared thermal imaging

Abstract: In intelligent agricultural machinery, automatic navigation, and visual perception technology have been developed rapidly in recent years, and they also play a vital role in intelligent modern agriculture. Therefore, real-time semantic segmentation of farmland environment become an important part of visual environment perception in the intelligent agricultural machinery. The visible light sensing equipment is mainly used for image collection. However, particularly in the dark environment at night, the deficient imaging effect of visible light cameras can result in a decrease in the accuracy of semantic segmentation. Infrared thermal imaging can offer an alternatively way in this case, due to this technology uses the temperature difference of the object for imaging, rather than the light source. Therefore, the infrared thermal imaging can be used to clearly capture the image in the dark night, rain, mist, smoke, and other visible light sensing equipment that is not suitable. In this study, a method for real-time semantic segmentation of infrared images of farmland environment at night was proposed using the infrared thermal imaging system. An infrared real-time bilateral semantic segmentation network (IR-BiSeNet) was also addressed suitable for infrared images, in order to ensure the accuracy and real-time performance of infrared image semantic segmentation in the farmland environment at night. According to the characteristics of low resolution and fuzzy details of infrared images, the network was improved based on the BiSeNet structure, and the low-level features of infrared images were further integrated in its spatial path. In the network, the global maximum pooling layer was used to replace the global average pooling layer in the attention enhancement and the feature fusion module, in order to preserve the texture details of infrared image. The infrared farmland data was collected by the infrared thermal imaging to create a dataset at night, thereby to train a semantic segmentation model suitable for the farmland environment in this case. The segmentation targets of dataset included the fields, pedestrians, plants, obstacles, backgrounds, using the data augmentation to produce the dataset of infrared night farmland. Five representative semantic segmentation methods were selected to verify the proposed method, including BiSeNet、DenseASPP、DeeplabV3+、DFANet, and CGNet. Experimental results showed that the proposed method can achieved the mean intersection over union of 85.1%, and the processing speed of 40 frames/s. The method proposed in this study can be used the infrared thermal imaging to perform real-time farmland environment semantic segmentation at night, which can greatly improve the visual perception of intelligent agricultural machinery at night.