In the face of the challenges and security risks posed by the complex environment at the border, the deployment of unmanned monitoring systems is crucial for our country′s border defense. The existing unmanned border defense systems encounter challenges such as high rates of false positives and missed detections, as well as insufficient real-time capabilities, primarily due to significant variations in image scale caused by differences in distance between the cameras and the intrusion targets, along with the use of occlusion strategies by the monitored targets. A FDB-YOLOv5 occlusion small target detection algorithm with higher average accuracy, fewer parameters, and stronger universality is proposed to address this issue. Firstly, a dataset is constructed by collect a large number of personnel samples with different occlusion areas; secondly, a new structure called Faster_C3 has been introduced to reduce the delay and parameter count of the occlusion small target detection network, thereby improving the detection speed and universality of the model; in addition, a Dysample upsampler based on point sampling is introduced into the neck network to obtain more local details and semantic information, enhance the detection capability of the model for small targets, while reducing the computational overhead. Finally, a spatial pyramid pooling method based on multi-scale feature extraction BSPPF is used to effectively solve the problems of scale invariance and loss of feature information of the occluded targets, so as to better capture key information and im-prove the stability and robustness of the model for detecting occluded small targets. The experimental results indicate that compared to the baseline YOLOv5, FDB-YOLOv5 mAP@0.5% reaching 91.5%; experimental outcomes demonstrate that compared to the baseline YOLOv5, FDB-YOLOv5 exhibited superior performance with an mAP@0.5 score reaching 91.5%. There was also a reduction in the number of parameters and computations by 19.07% and 18.41%, respectively, and an increase in model detection speed by 8.83%. When compared to Faster R-CNN、SSD、YOLOv5s and YOLOv8, FDB-YOLOv5 showcases outstanding capabilities, offering valuable insights for unmanned border target detection technologies.