Multi-license plate detection in complex scenes is still a challenging task because of multiple vehicle license plates with different sizes and classes in the images having complex background. The edge features of high-density distribution and the high curvature features of stroke turning of Chinese character are important signs to distinguish Chinese license plate from other objects. To accurately detect multiple vehicle license plates with different sizes and classes in complex scenes, a multi-object detection of Chinese license plate method based on improved YOLOv3 network was proposed in this research. The improvements include replacing the residual block of the YOLOv3 backbone network with the Inception-ResNet-A block, imbedding the SPP block into the detection network, cutting the redundant Inception-ResNet-A block to suit for the multi-license plate detection task, and clustering the ground truth boxes of license plates to obtain a new set of anchor boxes. A Chinese vehicle license plate image dataset was built for training and testing the improved network, and the location and class of the license plates in each image were accurately labeled. The dataset has 62,153 pieces of images and 4 classes of China vehicle license plates, almost images have multiple license plates with different sizes. Experiments demonstrated that the multi-license plate detection method obtained 83.4% mAP, 98.88% precision, 98.17% recall, 98.52 F1 score, 89.196 BFLOPS and 22 FPS on the test dataset, and whole performance was better than the other five compared networks including YOLOv3, SSD, Faster-RCNN, EfficientDet and RetinaNet.

Medical image segmentation is fundamental for delineating lesion and organ boundaries in clinical workflows. While UNet-based models remain widely used, CNN-dominant designs are limited in modeling long-range context, and Transformer-based variants often introduce substantial computational overhead due to quadratic attention. To address this issue, we propose KMP-UNet, a parallel U-shaped framework that combines a Mamba-based state-space branch for linear-complexity contextual modeling and a Kolmogorov–Arnold Network (KAN) branch for nonlinear feature representation. We further introduce a task-oriented fusion block and a skip refinement module to better exploit hierarchical encoder–decoder features. KMP-UNet has a compact model size (about 1.0M parameters in our implementation). We evaluate the proposed method on four public datasets (ISIC2017, ISIC2018, CVC-ClinicDB, and BUSI) using standard segmentation metrics. On ISIC2018, KMP-UNet achieves 0.9038 DSC and 0.9600 accuracy under our protocol. Extensive comparisons and targeted ablations are conducted to analyze the contribution of each component.