A new adaptive CFAR (Constant False Alarm Rate) detector based on CA/GO/OS three-dimensional fusion is proposed in this paper. The detector integrates the best detection performance of CA-CFAR, GO-CFAR and OS-CFAR in homogeneous environment, clutter edge environment and multi-target environment respectively, and uses the convex hull learning algorithm to use the decision convex hull for target detection in three-dimensional space. Through detailed simulation analysis in homogeneous environment, multi-target environment, clutter edge environment, multi-target and clutter edge simultaneous environment, it is proved that the detection performance of the CFAR detector in different environments has maintained a very high level and has a good ability to adapt to clutter environment.
There are inevitable problems in the imaging mechanism of visible and infrared images, which bring more difficulties to the detection task. Firstly, this paper introduces the principle of SSD one-stage detection model. Aiming at the characteristics and problems of the model, residual network is used to improve it, and the prediction feature layer is reselected and sorted. Based on the photoelectric equipment, the video data of marine ship targets are collected and the data set is constructed. Through the comparison of the training results of various models, it is found that the detection accuracy of small-sized targets is low. In order to solve this problem, First, reduce the size of the anchor , and the feature extraction network is modified. The experimental results show that the average detection accuracy of the improved model increases by 7%, and the single category accuracy of small targets increases to 87%.
In the complex remote sensing image detection, there are still many challenges in maritime ship detection. This paper combines the latest swin-transformer model with satellite remote sensing big data, uses the SSDD dataset, researches for maritime ship target detection, realizes the target detection support for SSDD dataset, at the same time, this paper proposes an improved version of augmix method Multiple-augmix, based on the principle of data enhancement for SAR image dataset, is proposed to achieve target detection and data enhancement for SSDD dataset and make comparison experiments. The detection accuracy of swin-transformer can reach 86.5% for the original SSDD dataset, and 96.2% for the SSDD dataset enhanced with Multiple-augmix method, which is 10 percentage points higher than that before enhancement, and at the same time, by conducting experiments with Fasterrcnn, RetinaNet and Focos algorithms and making comparisons, the experiments prove that This framework can achieve high detection accuracy in remote sensing image detection, and the Multiple-augmix method proposed in this paper is very effective in improving the detection accuracy of SSDD dataset. In addition, we verify the effectiveness, stability and accuracy of the above algorithm for SAR image target detection in complex remote sensing scenarios.
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