Research On Image Inpainting Technology For Industrial Production Environment Monitoring

As one of the basic technologies in the field of computer vision,image inpainting can effectively reconstruct the missing and damaged pixels in the image,try to restore the original texture and structure of the image,and play an important role in the fields of target detection,target tracking,and intelligent monitoring.Nowadays,image restoration technology based on deep learning has been developed rapidly,especially the generative adversarial network GAN has been widely studied,which is the hot spot of image inpainting technology.Due to the imperfection of the network structure of GAN itself,Traditional GAN has problems such as low repair efficiency,blurry repair results for large-area defective images,and poor discriminator identification ability.In order to solve the above problems,this paper will optimize the image restoration process and GAN network,the main work is as follows:(1)The BA-YOLO(Box-Attention yolo)algorithm is proposed to detect the repaired area.In order to avoid the waste of resources caused by the repair of the entire image,this paper uses the yolov5 network to detect the area to be repaired,and only input the area to be repaired into the image repair network in the next step to improve the repair efficiency.In order to improve the detection accuracy of yolov5 in the repair area under complex background and multi-noise conditions,this paper integrates the boxattention mechanism into the yolov5 network and proposes the BA-YOLO algorithm,which enhances the attention to key features and reasonably removes redundant features,thereby effectively improving the ability of yolov5’s feature network to distinguish between target objects and backgrounds and eliminating the interference of irrelevant areas.Compared with the yolov5 network without attention mechanism,BAYOLO has improved the performance indicators of object detection,such as precision,recall,and AP.(2)An algorithm that introduces a co-modulation mechanism and a double contrast loss function in GAN to optimize the image repair performance is proposed.Aiming at the problem that the traditional GAN network has poor repair effect on large-area damaged images,this paper introduces co-modulated GANs into GAN to improve the repair performance of the repair network for large-area defective images by combining the advantages of controllable image style generated by conditional GAN and the strong random generation ability of nonconditional GAN images.At the same time,in view of the shortcomings that the loss function of traditional GAN is insufficient generalization for the feature representation of the discriminator and cannot stimulate the adversarial evolution of the generator,this paper uses the Dual Contrastive Loss function in GAN on the basis of introducing the co-modulation mechanism to enhance the discrimination ability of the discriminator and have the ability to confront the generator.The optimized image restoration algorithm in this paper is compared with ordinary GAN,DCL-GAN and CMGAN algorithms,and the performance of PSNR,SSIM and FID for image restoration is improved.(3)The design and implementation of industrial environmental monitoring system is completed;The algorithm designed in this paper is encapsulated as API through the back-end development technology and integrated into the system with web development,which verifies the feasibility of the algorithm proposed in this paper in the field of industrial environment monitoring.