This paper concentrates on the elevator passenger detection task, a pivotal element for subsequent elevator passenger tracking and behavior recognition, crucial for ensuring passenger safety. To enhance the accuracy of detecting passenger positions inside elevators, we improved the YOLOv8 network and proposed the SC-YOLOv8 elevator passenger detection network with soft-pooling and attention mechanisms. The main improvements in this paper encompass the following aspects: Firstly, we transformed the convolution module (ConvModule) of the YOLOv8 backbone network by introducing spatial and channel reconstruction convolution (SCConv). This improvement aims to reduce spatial and channel redundancy in the feature extraction process of the backbone network, thereby improving the overall efficiency and performance of the detection network. Secondly, we propose a dual-branch SPP-Fast module by incorporating a soft-pooling branch into the YOLOv8 network’s SPP-Fast module. This dual-branch SPP-Fast module can preserve essential information while reducing the impact of noise. Finally, we propose a soft-pooling and multi-scale convolution CBAM module to further enhance the network’s performance. This module enhances the network’s focus on key regions, allowing for more targeted feature extraction, thereby further improving the accuracy of object detection. Additionally, the attention module enhances the network’s robustness in handling complex backgrounds. We conducted experiments on an elevator passenger dataset. The results show that the precision, recall, and mAP of our improved YOLOv8 network are 94.32%, 91.17%, and 92.95%, respectively, all surpassing those of the original YOLOv8 network.

In this paper, we propose an efficient target detection algorithm, ASF-Sim-YOLO, to address issues encountered in escalator step defect detection, such as an excessive number of parameters in the detection network model, poor adaptability, and difficulties in real-time processing of video streams. Firstly, to address the characteristics of escalator step defects, we designed the ASF-Sim-P2 structure to improve the detection accuracy of small targets, such as step defects. Additionally, we incorporated the SimAM (Similarity-based Attention Mechanism) by combining SimAM with SPPF (Spatial Pyramid Pooling-Fast) to enhance the model’s ability to capture key information by assigning importance weights to each pixel. Furthermore, to address the challenge posed by the small size of step defects, we replaced the traditional CIoU (Complete-Intersection-over-Union) loss function with NWD (Normalized Wasserstein Distance), which alleviated the problem of defect missing. Finally, to meet the deployment requirements of mobile devices, we performed channel pruning on the model. The experimental results showed that the improved ASF-Sim-YOLO model achieved an average accuracy (mAP50) of 96.8% on the test data set, which was a 22.1% improvement in accuracy compared to the baseline model. Meanwhile, the computational complexity (in GFLOPS) of the model was reduced to a quarter of that of the baseline model, while the frame rate (FPS) was improved to 575.1. Compared with YOLOv3-tiny, YOLOv5s, YOLOv8s, Faster-RCNN, TOOD, RTMDET and other deep learning-based target recognition algorithms, ASF-Sim-YOLO has better detection accuracy and real-time processing capability. These results demonstrate that ASF-Sim-YOLO effectively balances lightweight design and performance improvement, making it highly suitable for real-time detection of step defects, which can meet the demands of escalator inspection operations.

The reliable and safe operation of traction elevators depends on traction capacity, which is degraded by traction sheave groove wear. The resulting slippage reduces transmission efficiency and may cause a catastrophic failure due to the sudden loss of friction. After analyzing slippage mechanisms, we propose a prediction model that combines the Improved Pelican Optimization Algorithm (IPOA) with an Extreme Learning Machine (ELM). A mechanism analysis identifies key inputs—the wear amount, payload, and wire rope tension—providing a basis for model construction. The approach uses Halton sequence initialization, adaptive nonlinear weighting, and Gaussian perturbation, which improve the handling of nonlinearities. IPOA is then employed to optimize the ELM parameters, yielding the IPOA-ELM model. Experiments across multiple wear conditions show that IPOA-ELM predicts slippage more accurately than a traditional ELM. The study clarifies how traction sheave groove wear induces rope slippage and demonstrates the effectiveness of the proposed model under varying wear and load conditions, offering a practical reference for failure mechanism analysis and preventive strategies in elevator traction systems.