Remote Sensing Image Vehicle Detection Based on Pre-Training and Random-Initialized Fusion Network

Vehicle detection based on remote sensing images is of great significance to intelligent traffic management and smart city planning. The performances of the mainstream vehicle detection methods such as Fast-RCNN, SSD, FCRN, etc. are mainly affected by their extracted features, whose richness and robustness can be enhanced by introducing pre-training models from other datasets. However, the weights of the pre-training models used in traditional methods like FCRN, only shallow-tuned from the weights of another database training, without deep optimization from random initialization. This letter proposes a new pre-training and random-initialized fusion network (PRFN): the random initialization model and the pre-training model are connected in parallel, and an encoder is used to fuse their features, thereby improving the richness of features and the robustness of the model. We evaluate four different combination patterns of the pre-training and random initialized models, and the effect of the fusion feature encoder. Experiments show the parallel concatenating pattern with the fusion feature encoder achieves the best performance than other patterns. We test on three datasets, our PRFN exceeds the traditional methods, and, respectively, 1.23%, 1.04%, 0.94% higher than the baseline. All codes are available from https://github.com/LHKRobert/PRFN.