This study proposes an improved detection algorithm of YOLO V3 specially applied in small target detection to solve the problems of low detection and high false alarm rates of small targets in an image. The resolution of small targets is low, and their features are not obvious; thus, this study proposes 2× upsampling for the feature map down-sampled by 8× of the previous network,and the feature map upsampled by 2× is concatenated with the output of the second ResNet block unit. A feature fusion target detection layer, whose feature map is down-sampled by 4×, is established. Two ResNet units in the second ResNet block unit of Darknet53 in the YOLO V3 network structure are added to obtain more features of the small target. The K-means clustering algorithm is used to select the number of candidate anchor boxes and aspect ratio dimensions. A comparative experiment is performed based on the improved YOLO V3 algorithm on the VEDAI dataset and YOLO V3 algorithm. The results show that the improved YOLO V3 algorithm can efficiently detect small targets and improve the mean average precision and recall rate of small targets.