Abstract: Two issues arising out of the small objects, detected within UAV remote sensing images, sometimes happening to be multiple and dense increases in both the misdetection and miss out rates. A lightweight target detection algorithm is proposed based on YOLO v4 with a dense connection network module to realize high precision identification of the small targets. Firstly, feature reuse and feature extraction enhancement are facilitated by adjusting the convolutional layers of CSP Darknet53 (backbone network of YOLO v4) to work in two modes of actions:dense connection and sparse connection; the gradient disappearance problem is also alleviated. Secondly, the proposed module tailors the models to fit into a reduced number of network layers; then the resulting module is redefined as the new densely connected network module. Experiments are carried out on the NWPU-VHR-10 and Vihicle-850 UAV image datasets, a courtesy of our research group. Our model has the upper hand in terms of the number of the accurately detected small targets within the remote sensing images; our implementation is also cost-effective in terms of the network model convergence time and memory consumption. A significant improvement in the speed of detection is gained.

Key words: object detection, unmanned aerial vehicle remote sensing images, YOLO v4, lightweight, dense connection