3D-MGLNET: MOVING VEHICLE DETECTION IN SATELLITE VIDEOS WITH 3D MOTION-GUIDED LIGHTWEIGHT NETWORK

Object detectors based on convolutional neural networks have been widely-applied to detect moving vehicles in satellite videos. However, many detectors render superior detection accuracy at the expense of increased computational complexity and decreased inference speed. This prevents these detectors from being deployed into mobile devices. In this paper, an efficient 3D motion-guided lightweight network (3D-MGLNet) is proposed. Specifically, 3D-MGLNet constructs a motion-guided module based on 3D convolution to extract motion cues from spatial-temporal information. This module uses model compression strategies to detect moving vehicles in real-time while following the principle of "fewer channels, smaller convolution kernels," significantly reducing the number of parameters and computational complexity. Extensive experiments are conducted on the Jilin-1 and SkySat satellite video datasets. The results demonstrate that 3D-MGLNet gains strong performance by striking an excellent tradeoff between resource and accuracy, resulting in the fewest parameters (0.35M) and fastest speed (66.84 fps) compared to other popular models.