In the field of automatic driving, existing target detection algorithms are haunted by low detection precision due to complicated road backgrounds and insufficient information about small targets. Since the onboard camera has fixed viewing angles, and targets on the road are somewhat regularly distributed in the image space, richer information can be provided to autonomous vehicles for target detection. Therefore, a spatial feature augmentation network (SE-YOLOv5s) to improve YOLOv5s is proposed. A location attention module (LAM) is added to the neck network of YOLOv5s, which can be weighted according to the distribution characteristics of road targets in the image and enhance the network’s perception and localization ability for the target category location distribution. A small target enhancement module (STEM) is designed to fuse shallow features and deep ones, so as to obtain richer semantic information and detailed space information about small targets, thereby improving the detection effect of small targets. The results of the experiment show that the improved model witnesses an increase in detection precision against targets of different scales, with AP_S increased by 2.8%, AP_M increased by 2.5%, and AP_L increased by 2%.