Abstract: A contextual visual-based synthetic-aperture radar (SAR) target detection method and apparatus, and a storage medium, belonging to the field of target detection is described. The method includes: obtaining an SAR image; and inputting the SAR image into a target detection model, and positioning and recognizing a target in the SAR image by using the target detection model, to obtain a detection result. In the present disclosure, a two-way multi-scale connection operation is enhanced through top-down and bottom-up attention, to guide learning of dynamic attention matrices and enhance feature interaction under different resolutions. The model can extract the multi-scale target feature information with higher accuracy, for bounding box regression and classification, to suppress interfering background information, thereby enhancing the visual expressiveness.

Abstract: The present disclosure provides a synthetic aperture radar (SAR) image target detection method. The present disclosure takes the anchor-free target detection algorithm YOLOX as the basic framework, reconstructs the backbone feature extraction network from the lightweight perspective, and replaces the depthwise separable convolution in MobilenetV2 with one ordinary convolution and one depthwise separable convolution. The number of channels in the feature map is reduced by half through the ordinary convolution, features input from the ordinary convolution are further extracted by the depthwise separable convolution, and the convolutional results from the two convolutions are spliced. The present disclosure highlights the unique strong scattering characteristic of the SAR target through the attention enhancement pyramid attention network (CSEMPAN) by integrating channels and spatial attention mechanisms.

Abstract: The present disclosure provides a fire source detection method and device under the condition of a small sample size, and a storage medium, and belongs to the field of target detection and industrial deployment. The method includes the steps of acquiring fire source image data from an industrial site; constructing a fire source detection model; inputting the fire source image data to the fire source detection model, and analyzing the fire source image data via the fire source detection model to obtain a detection result, where the detection result includes a specific location, precision and type of a fire source. By means of the method, the problems of insufficient sample capacity and difficulty in training under the condition of a small sample size are solved, and different enhancement methods are used to greatly increase the number and quality of samples and improve the over-fitting ability of models.

Abstract: The present disclosure provides a fire source detection method and device under the condition of a small sample size, and a storage medium, and belongs to the field of target detection and industrial deployment. The method includes the steps of acquiring fire source image data from an industrial site; constructing a fire source detection model; inputting the fire source image data to the fire source detection model, and analyzing the fire source image data via the fire source detection model to obtain a detection result, where the detection result includes a specific location, precision and type of a fire source. By means of the method, the problems of insufficient sample capacity and difficulty in training under the condition of a small sample size are solved, and different enhancement methods are used to greatly increase the number and quality of samples and improve the over-fitting ability of models.

Abstract: A contextual visual-based synthetic-aperture radar (SAR) target detection method and apparatus, and a storage medium, belonging to the field of target detection is described. The method includes: obtaining an SAR image; and inputting the SAR image into a target detection model, and positioning and recognizing a target in the SAR image by using the target detection model, to obtain a detection result. In the present disclosure, a two-way multi-scale connection operation is enhanced through top-down and bottom-up attention, to guide learning of dynamic attention matrices and enhance feature interaction under different resolutions. The model can extract the multi-scale target feature information with higher accuracy, for bounding box regression and classification, to suppress interfering background information, thereby enhancing the visual expressiveness.

Abstract: The present disclosure provides a synthetic aperture radar (SAR) image target detection method. The present disclosure takes the anchor-free target detection algorithm YOLOX as the basic framework, reconstructs the backbone feature extraction network from the lightweight perspective, and replaces the depthwise separable convolution in MobilenetV2 with one ordinary convolution and one depthwise separable convolution. The number of channels in the feature map is reduced by half through the ordinary convolution, features input from the ordinary convolution are further extracted by the depthwise separable convolution, and the convolutional results from the two convolutions are spliced. The present disclosure highlights the unique strong scattering characteristic of the SAR target through the attention enhancement pyramid attention network (CSEMPAN) by integrating channels and spatial attention mechanisms.