Traffic sign recognition is an important part of intelligent transportation system. It uses computer vision and traffic sign recognition technology to detect and recognize traffic signs on the road automatically. In this paper, we propose a lightweight model for traffic sign recognition based on convolutional neural networks called ConvNeSe. Firstly, the feature extraction module of the model is constructed using the Depthwise Separable Convolution and Inverted Residuals structures. The model extracts multi-scale features with strong representation ability by optimizing the structure of convolutional neural networks and fusing of features. Then, the model introduces Squeeze and Excitation Block (SE Block) to improve the attention to important features, which can capture key information of traffic sign images. Finally, the accuracy of the model in the German Traffic Sign Recognition Benchmark Database (GTSRB) is 99.85%. At the same time, the model has good robustness according to the results of ablation experiments.

Forest fire has the characteristics of sudden and destructive, which threatens safety of people’s life and property. Automatic detection and early warning of forest fire in the early stage is very important for protecting forest resources and reducing disaster losses. Unmanned forest fire monitoring is one popular way of forest fire automatic detection. However, the actual forest environment is complex and diverse, and the vision image is affected by various factors easily such as geographical location, seasons, cloudy weather, day and night, etc. In this paper, we propose a novel fire detection method called Fire-PPYOLOE. We design a new backbone and neck structure leveraging large kernel convolution to capture a large arrange area of reception field based on the existing fast and accurate object detection model PP-YOLOE. In addition, our model maintains the high-speed performance of the single-stage detection model and reduces model parameters by using CSPNet significantly. Extensive experiments are conducted to show the effectiveness of Fire-PPYOLOE from the views of detection accuracy and speed. The results show that our Fire-PPYOLOE is able to detect the smoke- and flame-like objects because it can learn features around the object to be detected. It can provide real-time forest fire prevention and early detection.

Synthetic Aperture Radar (SAR) has become a highly regarded technique in the field of remote sensing due to its all-weather and all-time imaging capabilities. To address the limited generalization of SAR ship detection models caused by scarce public remote sensing data and restricted scenarios, this paper proposes an improved high-quality image generation framework Ship-SAR based on Denoising Diffusion Probabilistic Models (DDPM). Firstly, the denoising network UNet extracts the long-tail contours and multi-scale texture features of ships step by step by combining a reparameterization multi-branch structure with an efficient layer aggregation network. Secondly, this paper proposes a lightweight adaptive downsampling module that mitigates information loss in traditional downsampling processes by retaining task-relevant semantic information through a dynamic feature selection mechanism. Additionally, a depth spatial upsampling block is introduced to rearrange channel dimension information into the spatial dimension, which achieves resolution enhancement. Thirdly, Ship-SAR introduces an epsilon scaling strategy to mitigate input data bias between training and sampling stages. This approach corrects sampled trajectories without requiring retraining, significantly enhancing generation quality. Ship-SAR generates SAR ship images that compare favorably to baseline DDPM in terms of Fréchet inception distance metrics and structural similarity index measure. YOLOv11s achieved an increase in mAP from 87.4% to 90.9% on high-resolution SAR image datasets and an increase in mAP of 2.1% for RTDETR-r50. The comparative results show that the SAR ship images generated by Ship SAR significantly improve the diversity of the Ship-SAR image dataset and promote progress in the field of remote sensing detection.