Research of Road Scene Object Detection algorithm based on Mobile Platform

There are many object detection methods in terms of object recognition based on traditional methods, but they are not sufficient to meet the demand for accuracy and speed in real-life scenarios. And compared with mobile platform, cloud service is also not conducive to the use in practical scenarios. Therefor we optimize the YOLO (You Only Look Once, a method for real-time detection of objects) algorithm through renormalization processing, build the Chinese road sign dataset and perform random affine transformation, random blur, and brightness transformation processing on the dataset to enhance the generalization ability of the final model. The parameters of the model are fine-tuned to reduce the period required to train the model and improve the performance of deep learning. Finally, the deep learning model of object detection will be transplanted to iOS mobile terminal to meet the requirements of real-time and accuracy in automatic driving scenarios. We identifie three types of road objects. The detection accuracy of pedestrians on road scenes reaches 75.9%, and the average detection accuracy of buses, cars, bicycles, and motorcycles is 72%. The detection accuracy of road signs is 69%. Total accuracy is 74.31%. The average detection rate of running tests on mobile phones is 12.5 frames per second.