Shuqi Shen

Shuqi Shen, Junjie Yang

Safety helmets play a crucial role in protecting workers from head injuries in construction sites, where potential hazards are prevalent. However, currently, there is no approach that can simultaneously achieve both model accuracy and performance in complex environments. In this study, we utilized a Yolo-based model for safety helmet detection, achieved a 2% improvement in mAP (mean Average Precision) performance while reducing parameters and Flops count by over 25%. YOLO(You Only Look Once) is a widely used, high-performance, lightweight model architecture that is well suited for complex environments. We presents a novel approach by incorporating a lightweight feature extraction network backbone based on GhostNetv2, integrating attention modules such as Spatial Channel-wise Attention Net(SCNet) and Coordination Attention Net(CANet), and adopting the Gradient Norm Aware optimizer (GAM) for improved generalization ability. In safety-critical environments, the accurate detection and speed of safety helmets plays a pivotal role in preventing occupational hazards and ensuring compliance with safety protocols. This work addresses the pressing need for robust and efficient helmet detection methods, offering a comprehensive framework that not only enhances accuracy but also improves the adaptability of detection models to real-world conditions. Our experimental results underscore the synergistic effects of GhostNetv2, attention modules, and the GAM optimizer, presenting a compelling solution for safety helmet detection that achieves superior performance in terms of accuracy, generalization, and efficiency.

Hongliang Lu, Shuqi Shen, Junjie Yang et al.

More than the adherence to specific traffic regulations, driving culture touches upon a more implicit part - an informal, conventional, collective behavioral pattern followed by drivers - that varies across countries, regions, and even cities. Such cultural divergence has become one of the biggest challenges in deploying autonomous vehicles (AVs) across diverse regions today. The current emergence of data-driven methods has shown a potential solution to enable culture-compatible driving through learning from data, but what if some underdeveloped regions cannot provide sufficient local data to inform driving culture? This issue is particularly significant for a broader global AV market. Here, we propose a cross-cultural deployment scheme for AVs, called data-light inverse reinforcement learning, designed to re-calibrate culture-specific AVs and assimilate them into other cultures. First, we report the divergence in driving cultures through a comprehensive comparative analysis of naturalistic driving datasets on highways from three countries: Germany, China, and the USA. Then, we demonstrate the effectiveness of our scheme by testing the expeditious cross-cultural deployment across these three countries, with cumulative testing mileage of over 56084 km. The performance is particularly advantageous when cross-cultural deployment is carried out without affluent local data. Results show that we can reduce the dependence on local data by a margin of 98.67% at best. This study is expected to bring a broader, fairer AV global market, particularly in those regions that lack enough local data to develop culture-compatible AVs.