Geiger mode avanlanche photon diode （GM-APD） lidar has single photon detection sensitivity， which greatly reduces the system volume and power consumption. It makes the system feasible for practical application， and has become a hot topic in recent studies. However， owing to the limitation of the pixel number， the spatial resolution is low， which makes it difficult to obtain the clear contour of the remote target， and the object detection rate is not high. To solve this problem， a detection algorithm based on multi-level processing of the intensity and range images was proposed to find the correlation between the intensity images and point clouds’ features to improve the probability of small object detection. First， the improved feature pyramid network （FPN） combines the receptive field block （RFB） and convolutional block attention module （CBAM） with the feature extraction network to enhance the selection accuracy of intensity images. Second， the intensity and range images are combined into point clouds with intensity information in the candidate regions. Finally， a dynamic graph convolution network （DGCNN） is used to perform secondary detection on the target in the candidate regions. Moreover， point cloud information is used to further select the object in the candidate regions. In the GM-APD lidar long-range vehicle dataset， the AP of the network achieves 98.8%， and it has good robustness for complex scenes， such as incomplete vehicle structure， weak echo， and strongly reflected light spot. Compared with the SSD and YOLOv5， the detection accuracy of the network improved by 3.1% and 2.5%， respectively， which is feasible for lidar dim object detection.