Deep learning-based target detection method of border and coast defence

Abstract

The invention discloses a deep learning-based target detection method of border and coast defence, provides an FRCNN-based improved target detection network model, and belongs to the fields of targetdetection technology and computer vision. According to the method, for the problem that time consumed by detection of an original FRCNN algorithm is too long, a feature extraction structure in a detection network is redesigned, and an image classification data set after screening is utilized for retraining to obtain the image classification model of which a parameter number is lesser and a calculation amount is lesser. Deformable convolution is used to replace an original unique convolution layer, adaption ability of the detection network on object deformation is improved, and thus an averagedetection rate of a network structure is increased.

Description

technical field [0001] The invention belongs to the field of target detection, and in particular relates to a method for detecting border and coastal defense targets based on deep learning. Background technique [0002] Border and coastal defense work is an important guarantee for national territorial sovereignty and people's personal and property safety. Its responsibility is to maintain the stability of border and coastal areas and intercept illegal personnel and illegal targets from entering our territory. With the further deepening of my country's opening to the outside world, the flow of people entering and leaving the country continues to increase, and criminal activities such as smuggling and smuggling on the border are becoming increasingly rampant. The traditional way of relying on manpower for border defense can no longer meet the security requirements of border and sea defense in the current form. Therefore, it is particularly urgent to build an intelligent borde...

AI Technical Summary

Problems solved by technology

The environment of my country's border defense area is complex, the border line is extremely long, and the distribution of key monitoring areas such as border defense monitoring stations and entry-exit ports is relatively scattered, resulting in a large amount of monitoring data that cannot be processed in time, resulting in a great waste of video information resources

At present, most of the surveillance videos need to be manually processed by border guards. Its efficiency is too low, and the front-end and back-end do not support automatic detection of targets. The cutting-edge technology applied is only the extraction of key frames of the video. Unable to meet the needs of timely and effective processing of large amounts of data

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