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Target detection is the basic technology of automatic driving system. Deep learning has gradually become the mainstream target detection algorithm because of its powerful feature extraction ability and adaptive ability. How to ensure accuracy and speed is a great challenge in the field of target detection. In order to solve the problems of high miss detection rate of small target and difficult to realize embedded real-time detection in the process of complex environment detection by deep learning method, this paper adds two auxiliary remaining network blocks in the backbone network. So that the backbone network can extract the global and local features of the detected object, and carry out feature extraction based on the feature pyramid network Fusion, adding a scale to form a three scale prediction, to improve the problem of poor detection accuracy of yolov4-tiny network. The simulation results show that: Compared with yolov4-tiny, the accuracy of the improved network structure is improved by 3.3%, and the detection speed is 251 fps, which ensures the requirements of real-time detection. This algorithm has good detection effect in the case of lack of illumination and target occlusion, and its detection accuracy on the mixed data set is better than that of the contrast algorithm, which meets the real-time detection conditions and is suitable for deployment on the embedded system carried by the car.

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In the past ten years, multimodal image registration technology has been continuously developed, and a large number of researchers have paid attention to the problem of infrared and visible image registration. Due to the differences in grayscale distribution, resolution and viewpoint between two images, most of the existing infrared and visible image registration methods are still insufficient in accuracy. To solve such problems, we propose a new robust and accurate infrared and visible image registration method. For the purpose of generating more robust feature descriptors, we propose to generate feature descriptors using a concentric-circle-based feature-description algorithm. The method enhances the description of the main direction of feature points by introducing centroids, and, at the same time, uses concentric circles to ensure the rotation invariance of feature descriptors. To match feature points quickly and accurately, we propose a multi-level feature-matching algorithm using improved offset consistency for matching feature points. We redesigned the matching algorithm based on the offset consistency principle. The comparison experiments with several other state-of-the-art registration methods in CVC and homemade datasets show that our proposed method has significant advantages in both feature-point localization accuracy and correct matching rate.