Research On Performance Optimization Of Object Detection Algorithms For Specifc Scenarios

With the continuous development of deep learning and the continuous improvement of hardware computing capabilities,computer vision has attracted more and more researchers' attention.Computer vision allows machines to have the capabilities of human vision and the ability to perceive images.Object detection is a research hotspot in computer vision.Its main goal is to provide classification information and coordinate information of the object to be detected at the same time in the image.In practical applications,many other tasks are performed based on the results of object detection.Therefore,the performance of the object detection task will directly affect the performance of subsequent tasks,and then the effect of the final practical application.At the same time,faster detection speed can make applications better meet real-time requirements.Therefore,the optimization of the accuracy performance and time performance of the object detector is very meaningful for practical applications.The purpose of this paper is to optimize the performance of the object detection algorithm and design an object detector that is more suitable for practical applications.Generally speaking,there is a mutual constraint relationship between accuracy and speed,and it is difficult for general optimization to improve both.Through investigation,we found that many practical application scenarios have the characteristics of less categories to be detected and relatively fixed background.Therefore,this paper aims at such specific scenarios,optimizing the detection accuracy and detection speed of the object detection algorithm at the same time.The main contributions are as follows:(1)According to the characteristics of the scene defined in this paper,the convolution kernel of the object detection model is trimmed to reduce the network width while retaining the network depth.The detection speed has been doubled,and the accuracy has fallen within an acceptable range.(2)An Adaptive Focal Loss function is proposed,eliminating the need to manually set the hyperparameters,so that the network can adaptively adjust the hyperparameters based on the convergence during the training process,making the network training results better and improving the model detection accuracy.(3)According to the different characteristics of different application scenarios and different requirements for accuracy and recall,the original Greedy-NMS based non-maximum suppression algorithm was improved,and two improved algorithms were proposed:Precise-NMS and Better-NMS,which is more suitable for scenarios with high accuracy and high recall respectively.Finally,experiments were performed on the data sets of the gas station,coffee shop,and autonomous driving.The object detection model speed increased by 5.5 times to 192 FPS,and mAP increased by 2.47,2.34,and 0.3 1,respectively.