Vehicle Detection in High Resolution Satellite Remote Sensing Images Based on Deep Learning

With the exponential growth of the number of vehicles, a series of traffic system problems continue to emerge. Vehicle detection plays an important role in the traffic system. Researchers have invested a lot of energy on it, and have made many achievements. However, there are still some deficiencies in the accuracy and robustness. The development of satellite remote sensing technology and VR panoramic technology provides technical support for this study. In order to detect vehicles more accurately, provide accurate and effective data information to relevant departments, and improve traffic conditions, this study uses deep learning algorithm to detect vehicles in high-resolution satellite remote sensing images. Firstly, the images in the images are classified by using the Alexnet network model, and then the vehicle target detection ability of the Faster R-CNN model algorithm is tested, and the algorithm is optimized by the method of model pruning and quantization, so that the average accuracy rate reaches 70.34%, and has strong robustness. Then the algorithm model is applied to the practical application of vehicle detection at an intersection. The traffic flow and the number of speeding vehicles are detected, and the false detection rate and missing detection rate are calculated. The results showed that the rate of missed detection and false detection was 0% on December 19, and the highest rate was 4.5% and 2.7% respectively. This shows that the vehicle detection based on deep learning algorithm in high resolution satellite remote sensing images has high accuracy, can effectively detect speeding vehicles, and can play a normal role in practical applications.