Research On Cargo Image Recognition Method In Complex Environment

As one of important components of an integrated security system,automatic inspection plays a role in avoiding external threats to important materials through image recognition technology.The storage environment of protected materials is complex,and when identifying,it is affected by factors such as shooting angle,illumination and occlusion,resulting in the problems of small target,large background interference,low resolution and insufficient recognition accuracy in the captured image.Aiming at the above problems,the method of cargo image recognition in complex environment is studied in thesis.It mainly includes the following three research contents:(1)A fusion target detection and ESRGAN image recognition method was studied.To overcome the problems of small targets,high background interference and low resolution of the proprietary image dataset Cargo images,an image recognition method TEResNet(target-decetion and ESRGAN before ResNet)was proposed.The method mainly had three steps:firstly,the target image was obtained by using the target detection method;then ESRGAN(Enhanced super-resolution Generative Adversarial Networks)model was used to improve the image Resolution after target detection;finally,the improved ResNet model was used for image recognition.Comparative experiments were conducted on three public datasets and one proprietary dataset.The experimental results showed that the TEResNet method had higher recognition accuracy than ResNet,AlexNet,GoogleNet and MobileNet.(2)A new post-image recognition method,named CISCGAN(Compute Image Similarity and Conditional Generative Adversarial Network),was proposed for the image recognition failure of TEResNet method.CISCGAN had three steps;firstly,according to the mean square error,peak signal-to-noise ratio and structural similarity,a sample image with the highest similarity to the failed image was selected from recognition correct samples in the training sample image set;then inputted the selected image into the CGAN model to generate a new image;finally,the image generated by CGAN model was recognized by TEResNet method.Comparative experiments were conducted on a proprietary dataset.Experimental results showed that CISCGAN method can improve the accuracy of image recognition.(3)Based on the above two research results,an automatic inspection system with image recognition function was developed.The system was developed by employing Microsoft Visual Studio 2012 and Microsoft SQL Server 2012 Databases.Image recognition was implemented with the help of the deep learning framework PyTorch.The system test results showed that the image recognition accuracy met the requirement of design.Through the above research results,a simulated cargo image recognition model of the automatic inspection system for complex environment was constructed.The system real-time inspected important materials automatically with accurately and reliably.