YOLOv8-PEL shows outstanding performance in detection accuracy, computational efficiency, and generalization capability, making it suitable for real-time and resource-constrained applications. This study aims to address the challenges of vehicle detection in scenarios with fixed camera angles, where precision is often compromised for the sake of cost control and real-time performance, by leveraging the enhanced YOLOv8-PEL model. We have refined the YOLOv8n model by introducing the innovative C2F-PPA module within the feature fusion segment, bolstering the adaptability and integration of features across varying scales. Furthermore, we have proposed ELA-FPN, which further refines the model’s multi-scale feature fusion and generalization capabilities. The model also incorporates the Wise-IoUv3 loss function to mitigate the deleterious gradients caused by extreme examples in vehicle detection samples, resulting in more precise detection outcomes. We employed the COCO-Vehicle dataset and the VisDrone2019 dataset for our training, with the former being a subset of the COCO dataset that exclusively contains images and labels of cars, buses, and trucks. Experimental results demonstrate that the YOLOv8-PEL model achieved a mAP@0.5 of 66.9% on the COCO-Vehicle dataset, showcasing excellent efficiency with only 2.23 M parameters, 7.0 GFLOPs, a mere 4.5 MB model size, and 176.8 FPS—an increase from the original YOLOv8n’s inference speed of 165.7 FPS. Despite a marginal 0.2% decrease in accuracy compared to the original YOLOv8n, the parameters, GFLOPs, and model size were reduced by 25%, 13%, and 25%, respectively. The YOLOv8-PEL model excels in detection precision, computational efficiency, and generalizability, making it well-suited for real-time and resource-constrained application scenarios.

With the development of autonomous driving technology, vision geo-localization has obtained a consistently growing following. How to match correct image pair from different perspectives is the key technology. Existing geo-localization methods focus on designing complex attention mechanism based on traditional backbone, e.g., VGG, ResNet, but neglect the importance of backbone network. In this article, we propose a modern backbone based geo-localization method (MBEG). MBEG introduces the latest vision fundamental network EVA-02 as backbone, which has been fully trained in large datasets. In addition, the feature rotate encoding strategy is presented to eliminate the effects of image rotation. We also apply the knowledge distillation to squeeze network's parameters for actual application. Our work exhibited excellent performance on the University-1652 dataset, and our solution attained the top-1 ranking in the UAVs in Multimedia Challenge for the University-160k dataset.

Aiming at the low-carbon cooperation between service providers and demanders in the cloud manufacturing mode, a tripartite stochastic evolutionary game model of low-carbon cooperation among service providers, demanders and the cloud platform is established. According to the stability criterion theorem of stochastic differential equations, the conditions for strategy evolutionary stability are analyzed. Finally, the impacts of important factors on the strategy evolution are analyzed, such as reward and punishment of cloud platform. Simulation shows that: low-carbon costs and random factors have a negative impact on low-carbon cooperation between providers and demanders, and the appropriate rewards and punishments have a positive impact on low-carbon cooperation between providers and demanders.