Research And Implementation Of Remote Sensing Image Detection Technology Based On CNN

Convolutional neural networks(CNNs)have become increasingly significant in numerous application areas,owing to the continuous advancement of hardware device performance and the ongoing research and development of deep learning technology.The algorithmic model for object detection using CNNs has reached a high level of maturity,and the general object detection algorithm has gained widespread adoption in daily settings.Nonetheless,the detection of objects in remote sensing image scenes is confronted with multiple challenges,including the small size of detection objects,variations in object scales and directions,and the significant impact of background regions on object detection.These difficulties set remote sensing image scenes apart from general-purpose object detection scenes.This article aims to improve general detection models for remote sensing image scenes and apply them to similar characteristic scenes,considering the technical challenges in remote sensing image scenarios.(1)This paper aims to enhance the Faster RCNN algorithm model and optimize its application in remote sensing image detection scenarios.In response to the technical challenges in object detection within remote sensing images,this study proposes a remote sensing detection model based on feature fusion and attention mechanisms.The model incorporates a multi-scale branch feature fusion module,a mixed attention module,and a rotation detection framework to enhance the detection performance of the model for objects in the images.Among them,the multi-scale branch feature fusion module integrates a multi-branch structure to enhance the detection performance of small objects;The mixed attention module focuses on the object of interest and reduces the negative impact caused by background noise;The rotation detection framework strengthens the alignment capability of the model for objects with arbitrary orientations.By conducting experiments on the DOTA dataset for both oriented bounding box(OBB)and horizontal bounding box(HBB)tasks,the superior performance of the proposed model in addressing technical challenges in remote sensing image object detection is demonstrated.The experimental results indicate that the average accuracy of the proposed model in this paper is 76.58%and 79.99%,respectively,which is 2.84%and 3.51%better than the control model.These results demonstrate that the model proposed in this paper has a stronger ability to cope with multi-scale,multi-directional,and background noise issues.Thus,it can provide a more effective solution for remote sensing image object detection.(2)In this study,the developed remote sensing image detection model was specifically aimed at intelligent mining applications.Leveraging the similarities between the technical challenges in coal mining scenarios and remote sensing image scenes,the model was developed,deployed,and tested in real mining environments.Once the monitoring system detects objects,it can provide different alarm information based on specific business requirements,thereby enhancing the intelligence level of the system.The proposed model demonstrates improved detection performance in certain scenarios,reducing the probability of false alarms in practical applications.This validates the practicality of the model and its ability to lower false alarm rates,thus increasing the overall effectiveness of the system.