Accurate and real-time detection of metro passengers is of utmost importance in ensuring public safety and facilitating efficient transportation management. However, this task remains challenging due to spatial constraints, complex backgrounds, scale variations, and mutual occlusion. In this article, we propose AMPDet—an anchor-free metro passenger detection model that features a dual-branch architecture. This architecture is designed to concurrently identify both the head and body regions of passengers. By leveraging this dual-branch architecture, AMPDet effectively prioritizes genuine pedestrian characteristics during its decision-making process, thus mitigating the intrusion of nonpedestrian elements. Additionally, we propose a head-body joint nonmaximum suppression (NMS) technique that combines the outputs of the two branches, aiming to efficiently recover suppressed body detections. Comprehensive experiments on CityPersons, CrowdHuman, and SH-metro show the superior performance of the proposed method in pedestrian detection, especially in crowded metro carriage scenes. Further, AMPDet showcases exceptional adaptability and efficiency on the NVIDIA Jetson AGX Xavier platform, affirming its practicality for implementation in metro passenger detection. Code and dataset will be available at https://github.com/weimingqi111/ SH-metro.

From the perspective of energy efficiency and environmental sustainability, the scheduling problem in a flexible workshop with the utilization of automated guided vehicles (AGVs) was investigated for material transportation. Addressing the dual-constrained integrated scheduling challenge involving machining machines and AGVs, a scheduling optimization model was established with makespan, workshop energy consumption, and processing quality as the optimization objectives. To effectively solve this model, an enhanced whale optimization algorithm (IWOA) was proposed. Specifically, nonlinear convergence factors, adaptive inertia weights, and improved helix positions were introduced into the standard whale optimization algorithm to update the model. Furthermore, a loss function was constructed based on fuzzy membership theory to obtain the optimal compromise solution of the multi-objective model. The research results indicate that: (1) The IWOA obtained the optimal solutions on benchmark instances MK01, MK02, MK04, MK07, and MK08; (2) The IWOA outperformed the WOA(1), WOA(2), WOA-LEDE, and NSGA-II algorithms in the two instances provided in this paper, demonstrating strong robustness of the model; (3) Although the multi-objective model constructed in this paper could not surpass the single-objective optimal solution in individual objectives, it achieved compensation in other objectives, effectively balancing the trade-offs among the makespan, workshop energy consumption, and processing quality of the three objectives. This research offers an effective practical approach to address green flexible workshop scheduling with AGV transportation.