Research On Enhancing Object Detection With Generative Adversarial Architecture

Object detection is a critical research area in computer vision that aims to identify and locate objects in images or videos.The paper proposes a new training method,Enhanced-GAN,for object detection that uses a generative adversarial architecture to enhance the feature extraction capabilities of the backbone network while conserving computer resources.The proposed model exploits the data distribution fitting capabilities of the generative adversarial architecture to enhance the feature extraction capabilities of the backbone network.As a result,the fully trained network can efficiently obtain training gradients,thus increasing the performance ceiling of the backbone network.In order to improve feature quality and balance in generative adversarial training,this paper introduces a deep convolutional network with parallel feature fusion architecture,called PB-Enhanced-GAN.The proposed model uses feature fusion technology to capture multi-scale features,which helps to ensure the convergence performance of the network.However,introducing a new backbone network to the architecture increases the number of network parameters.To prevent a decrease in inference speed,the paper proposes using a penalty matrix to replace the newly added network branch during detection and reduce detection complexity.In order to fully utilize the detection strengths of different targets by the main backbone network and supervision network,this paper also introduces an adaptive weight balance mechanism.The mechanism uses correlation between different types of feature outputs to model the detection ability of network branches for different targets and automatically adjusts the weight of supervision networks during forward propagation.Experimental results show that the proposed methods can effectively improve detector performance.Enhanced-GAN achieved a breakthrough in network performance bottlenecks and converged faster than the original training method of detection networks.Additionally,the feature fusion mechanism and penalty matrix introduced in PB-Enhanced-GAN effectively enhanced the convergence ability and inference speed of generative adversarial architecture.Furthermore,the adaptive weight balance mechanism improves the network’s ability to capture various types of features and enables it to adapt better to different detection objects.In summary,this paper improves object detection performance by introducing technologies based on generative adversarial architecture,parallel feature fusion architecture,and adaptive weight balance mechanism.These developments provide a substantial groundwork for further research in object detection techniques.