The use of unmanned aerial vehicles (UAVs) for effective real-time monitoring of small targets, such as people, cars, and objects in the scene area, can help maintain public security. To address the problems of small-target occlusion, overlapping, and interference of complex environments in UAV images, a small-target detection algorithm is proposed from the UAV perspective. The algorithm uses the YOLOX network as the baseline system. First, the neck part of the network increases the output feature graph to reduce the receptive field, thereby improving the performance of the network details, and the detection head of the small-sized feature graph is deleted to improve the detection rate of small targets. Second, the anchor-free association mechanism is used to reduce the influence of noise in the truth tag while simultaneously reducing the parameter setting to speed up network operations. Finally, a true proportion coefficient is proposed for small targets to calculate position loss, thereby increasing the penalty for misjudging small targets, which makes the network more sensitive to small targets. Experiments on the VisDrone2021 dataset using this algorithm showed that the mAP value increased by 4.56%; the number of parameters decreased by 29.4%; the amount of computation decreased by 32.5%; and the detection speed increased by 19.7% compared with those of the baseline system, which is an advantage over other mainstream algorithms.