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Infrared and visible images (multi-sensor or multi-band images) have many complementary features which can effectively boost the performance of object detection. Recently, convolutional neural networks (CNNs) have seen frequent use to perform object detection in multi-band images. However, it is very difficult for CNNs to extract complementary features from infrared and visible images. In order to solve this problem, a difference maximum loss function is proposed in this paper. The loss function can guide the learning directions of two base CNNs and maximize the difference between features from the two base CNNs, so as to extract complementary and diverse features. In addition, we design a focused feature-enhancement module to make features in the shallow convolutional layer more significant. In this way, the detection performance of small objects can be effectively improved while not increasing the computational cost in the testing stage. Furthermore, since the actual receptive field is usually much smaller than the theoretical receptive field, the deep convolutional layer would not have sufficient semantic features for accurate detection of large objects. To overcome this drawback, a cascaded semantic extension module is added to the deep layer. Through simple multi-branch convolutional layers and dilated convolutions with different dilation rates, the cascaded semantic extension module can effectively enlarge the actual receptive field and increase the detection accuracy of large objects. We compare our detection network with five other state-of-the-art infrared and visible image object detection networks. Qualitative and quantitative experimental results prove the superiority of the proposed detection network.

Infrared and Visible Image Object Detection via Focused Feature Enhancement and Cascaded Semantic Extension