Laser interference can interfere with target detection systems to achieve the goal of protecting the target. For example, it can obscure the target and change information such as brightness and contrast in the surrounding region of the interference zone. Therefore, it is necessary to analyze the impact of laser interference on target detection algorithms and evaluate the anti-interference performance of the algorithm. This paper analyzes the impact of laser interference on two target detection algorithms, Faster-RCNN and YOLO-V3, from two perspectives: target occlusion rate and target similarity. Then, a target-oriented method for dividing the effective region of laser interference (TODERLI) is proposed. The effectiveness of the algorithm is verified through experiments.
Most of the laser interfered image quality assessment algorithms need to know the reference images or partial information of reference images. However, in practical application, the reference image or its related information is difficult to obtain, which makes the application scenario of laser interference image quality evaluation algorithm is greatly limited. To solve this problem, this paper starts with the prediction processing of the obscured information and improves the Markov Random Field estimation algorithm (MRF) to realize the real-time estimation of the obscured area information. Then, proposes a non-reference image quality assessment method based on occlusion area information estimation and natural scene statistics (IENSS), which analyzes the statistical characteristics of laser interfered images in natural scenes. The model is trained by machine learning. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method.
At present, most of the full reference laser disturbing image quality assessment methods need to know the position information of the disturbing spot and the target in advance, so that the assessment process is restricted by the prior knowledge and the preprocessing method. Aiming at this problem, this paper proposes a laser disturbing image quality assessment method based on convolution feature similarity (CNNSIM), which analyzes the output features of the image before and after laser disturbing in the convolution network. The occlusion degree of key information in the disturbing image is assessed by using the hierarchy and the sensitivity to occlusion of features, thus avoiding the input requirement of target/spot location information. The simulation experiment verifies the effectiveness of the new assessment method in different scenarios.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.