Due to the large number of images， accurate and efficient target detection is the key step to enhance the automation of the shooting-range photometric image processing. Aiming at the problem of poor adaptability of traditional target detection algorithms due to multiple low-altitude target images and target types and changes in target characteristics， this paper proposes an automatic low-altitude target detection method based on dual-attribute classification deep learning. The method is based on YOLO V3， a deep learning target detection framework， and improves the single-attribute classification in the output layer of the network to dual-attribute classification based on the dual-attribute features of luminance and shape of the low-altitude target;achieves automatic sample annotation based on target region growth， and confirms the detection results using sequential image target constraints. The actual image training and detection results in the low-altitude scenario of the range show that the initial detection success rate of the method is higher than 90%， and 99% detection success rate and 62% average localization accuracy are achieved after post-processing.