Abandoned objects on the road significantly impact traffic safety. To address issues such as missed detections, false alarms, and localization difficulties encountered in detecting of small-to-medium-sized abandoned objects, this paper proposes a method for detecting and locating abandoned objects on the road using image guidance and point cloud spatial constraints. The method employs an improved YOLOv7-OD network to process image data, extracting information about two-dimensional target bounding boxes. Subsequently, these bounding boxes are projected onto the coordinate system of the LiDAR sensor to get a pyramidal region of interest (ROI). Under the spatial constraints of the point cloud within the ROI, the detection and localization results of abandoned objects on the road in three-dimensional space are obtained through a combination of point cloud clustering and point cloud generation algorithms. The experimental results show that the improved YOLOv7-OD network achieves recall and average precision rates of 85.4% and 82.0%, respectively, for medium-sized objects, representing an improvement of 6.6% and 8.0% compared to the YOLOv7. The recall and average precision rates for small-sized objects are 66.8% and 57.3%, respectively, with an increase of 5.3%. Regarding localization, for targets located 30-40 m away from the detecting vehicle, the depth localization error is 0.19 m, and the angular localization error is 0.082°, enabling the detection and localization of multi-scale abandoned objects on the road.