Research And Application Of Target Detection Algorithms Based On Convolutional Neural Network

Target detection technology is a research hotspot in the field of computer vision.With the rise of deep learning technology,the target detection algorithm based on convolutional neural network gradually replaces traditional target detection algorithms.These algorithms have high detection accuracy or good real-time performance.How to apply algorithms to the actual is also a research hotspot.In practical applications,some scene targets are difficult to detect and require high-precision algorithms.Some scenes require high real-time performance of the algorithm on mobile terminals.For the scenes with difficult detection,the aircraft in the remote sensing image is used as the detection target,and a high-precision target detection algorithm Dense-YOLO is proposed.Remote sensing target detection and analysis system is designed using this algorithm.In this paper,aiming at the driver's abnormal behavior,the target detection algorithm Realtime-YOLO which can be detected in real time on the embedded hardware platform is proposed by optimizing the Dense-YOLO algorithm.Using this algorithm,the driver behavior monitoring system is designed on the embedded hardware platform,NVIDIA Jetson TX2.The main work and innovative research results of this paper are as follows:1)In order to propose a high-precision target detection algorithm,the idea of multi-scale detection is introduced in the algorithm design.The K-means dimension clustering algorithm is used to cluster the aircraft target dataset in the self-built remote sensing images to select the most suitable detection scale and the anchor boxes size.In the basic network design of the algorithm,the idea of dense connection is adopted.A deep foundation network with residual connections and dense connections is proposed.This method is used to solve the gradient explosion problem caused by the network too deep and strengthen the transmission of information flow in the network.By combining the multi-scale detection method with the basic network,the high-precision target detection algorithm Dense-YOLO is designed.Experiments show that this algorithm is more accurate than other classical target detection algorithms when detecting aircraft targets in remote sensing images.Complete the remote sensing target detection and analysis system design on the computer by using this algorithm combined with the graphics class library QT.This system can quickly analyze the amount,size,position,image slice and other information in a remote sensing images.2)In order to propose an algorithm for real-time detection on embedded hardware platform,due to the limited resources of embedded hardware platform,a lightweight embedded real-time target detection algorithm Realtime-YOLO is proposed by optimizing the multi-scale detection mechanism and the basic network in the Dense-YOLO algorithm.Experiments show that this algorithm has higher detection accuracy under the premise of ensuring real-time detection of targets on the embedded hardware platform NVIDIA Jetson TX2.This algorithm is applied to complete the design of the driver's abnormal behavior monitoring system.This system has the functions of intelligently judging driver's abnormal behavior and voice alarm,screenshot forensics and so on.