7results about How to "Enhanced edge" patented technology

This invention relates to the field of target detection technology and discloses a method for obstacle detection in adverse weather conditions for AGV cargo vehicles used in copper electrode plate transfer. The method includes acquiring real-time images of the AGV vehicle to be detected under adverse weather conditions; inputting the real-time images to be detected into an improved YOLOX model for processing; and outputting obstacle detection results. The improved YOLOX model includes a backbone network, a feature pyramid network, a dynamic self-supervised network, and a detection head. The dynamic self-supervised network includes an environment-aware encoding layer and a dynamic feature reconstructing layer. Based on the improved YOLOX model, this invention solves the key technical problem of feature coupling caused by environmental interference in visual detection systems under adverse weather conditions by introducing a dynamic self-supervised network, and also addresses the core issue of a surge in false alarm rates in the AGV vehicle visual detection system during copper electrode plate transfer under adverse weather conditions.

The invention discloses a solid waste monitoring and data management system based on artificial intelligence, and relates to the technical field of water environment monitoring, the system is composed of an image acquisition module, a data preprocessing module, an encoder feature extraction module, an up-sampling fusion module, a detection head module and a data management module, and the system is based on an RW-YOLOv11 architecture. A C3K2Sc feature extraction unit is introduced into an encoder, and floating garbage edge and texture expression is enhanced through space attention and a dynamic channel reconstruction mechanism; a SurfCAU water surface content awareness enhanced up-sampling module is adopted in the neck network, detail compensation of low-resolution features is achieved, and the multi-scale feature fusion quality is improved; a SurfMSDFHead multi-branch structure is adopted in the detection head, multi-scale feature fusion is realized through dynamic weight, and the positioning precision of overlapped garbage is improved by using a Focaler-IOU interval weighting strategy. The system can realize real-time detection, classification and positioning of the floating garbage in the river channel, and has the advantages of high detection precision, good lightweight degree, strong adaptability to complex water surface scenes and the like.

This invention discloses an image enhancement method and system based on improved MSR and CLAHE, belonging to the field of image enhancement technology. The method involves extracting the reflection components from each channel using the improved MSR algorithm, quantizing them back to 0-255 to obtain R1, G1, and B1, and then performing CLAHE on the green channel G1 and the blue channel B1 to obtain G2 and B2, resulting in a color image img2. Guided filtering is then applied to img2, and a multi-scale method is used to extract and enlarge its detail information, achieving detail preservation. Finally, the image is converted to the HSV color space, and adaptive gamma correction is performed on the V channel to improve image brightness. The image enhancement method provided by this invention solves the problems of insufficient detail highlighting, low contrast, uneven brightness, and color distortion in existing medical image enhancement methods for cervical cancer images.

This invention belongs to the field of deep learning and image processing technology, and discloses a learnable tensor low-rank enhancement method for near-infrared images under low illumination. Based on the estimation results of the local brightness mean and noise variance of the input image, the image is non-uniformly adaptively divided, and a three-dimensional local feature tensor containing spatial height, width, and brightness channels is constructed on each image block using a lightweight convolutional feature extraction network. Subsequently, based on the brightness energy of the image block, the tensor decomposition rank of each block is adaptively determined; factor generation networks and kernel tensor prediction networks are used to generate directional factor matrices and kernel tensors, respectively; threshold suppression is applied to the singular values ​​of the kernel tensor to remove noise subspaces, and scaling of high-frequency directional factors is combined to enhance texture details, effectively restoring degraded edge structures under low illumination conditions. Finally, a weighted fusion method based on minimizing the brightness difference in overlapping regions is used to reconstruct the entire image from the restored image blocks.

The invention provides a circuit breaker appearance defect rapid detection method based on image recognition. The method relates to the technical field of image recognition, and comprises the steps that image data of the appearance of the circuit breaker are collected in real time through a camera, and the image data comprise overall shape data, detail feature data and defect areas of the appearance of the circuit breaker. According to the circuit breaker appearance defect rapid detection method based on image recognition, rapid and accurate detection of circuit breaker appearance defects is achieved through the image adaptive filtering technology, the physical modeling technology, the multi-scale feature extraction technology and other technologies. Specifically, the image quality is improved by using an image adaptive enhancement technology, then macroscopic and microscopic features are effectively extracted through a multi-scale feature extraction method, and finally a comprehensive feature image is formed in combination with a weighted fusion method. According to the method, the recognition capability of complex and irregular defects can be effectively improved, and a high-precision defect detection report is generated through a machine learning model.

This invention provides a method for generating lace texture images based on an ADM (Adaptive Demographic Model) and dynamic denoising filtering model. This model combines a learnable Gaussian blur layer with a high-pass filtering technique. The Gaussian blur layer adaptively adjusts the blur intensity based on the input image, reducing high-frequency noise in the lace texture image. The high-pass filtering technique enhances key areas such as the outline and edges of the main pattern in the image, thereby highlighting the main texture features. Adding this module to the downsampling layer of the ADM network improves the overly smooth appearance of the lace texture images generated by the original model and enhances the image's texture depth.