To address pedestrian occlusion and multi-scale issues in crowded scenes, a crowded pedestrian detection algorithm that integrates global context information, spatial information, and pose information is proposed. Firstly, the feature pyramid network (FPN) is enhanced by integrating a dynamic sampling module and a weighted fusion branch, enabling more effective multi-scale feature aggregation and better handling of pedestrian scale variations. Secondly, a global context information module is designed to capture additional potential pedestrian features by fusing global context information and spatial information, which mitigates feature loss due to occlusion. Additionally, an enhanced Transformer-based pose module is constructed to extract pose information as supplementary resources, tackling the challenges of occlusion in crowded pedestrian detection. Since pose estimation is limited by pose estimation algorithms, an improved CA attention is incorporated into the visual information to complement the pose information. Finally, a novel data augmentation method is proposed to simulate occlusion, thereby improving the model’s generalization ability. Extensive experimental evaluations on the CrowdHuman and CityPersons benchmarks corroborate the robustness and superior detection capabilities of the proposed algorithm, which consistently surpasses current state-of-the-art approaches in crowded pedestrian detection scenarios.

With the rapid proliferation of electric vehicles (EVs), urban logistics faces increasing challenges in optimizing vehicle routing. This paper presents a new modeling framework for the Electric Vehicle Routing Problem (EVRP), where multiple electric trucks serve a set of customers within their capacity limits. The model incorporates critical EV-specific constraints, including limited battery range, charging demand, and dynamic urban traffic conditions, with the objective of minimizing total delivery cost. To efficiently solve this problem, a Dual Population Cooperative Genetic Algorithm (DPCGA) is proposed. The algorithm employs a dual-population mechanism for global exploration, effectively expanding the search space and accelerating convergence. It then introduces local refinement operators to improve solution quality and enhance population diversity. A large number of experimental results demonstrate that DPCGA significantly outperforms traditional algorithms in terms of performance, achieving an average 3% improvement in customer satisfaction and a 15% reduction in computation time. Furthermore, this algorithm shows superior solution quality and robustness compared to the AVNS and ESA-VRPO algorithms, particularly in complex scenarios such as adjustments in charging station layouts and fluctuations in vehicle range. Sensitivity analysis further verifies the stability and practicality of DPCGA in real-world urban delivery environments.