An efficient improved YOLOv10 algorithm for detecting electric bikes in elevators

Tong Li; Lanfang Lei; Zhong Wang; Peibei Shi; Zhize Wu; Tong Li; Lanfang Lei; Zhong Wang; Peibei Shi; Zhize Wu

doi:10.3934/era.2025163

Electronic Research Archive

2025, Volume 33, Issue 6: 3673-3698. doi: 10.3934/era.2025163

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Research article

An efficient improved YOLOv10 algorithm for detecting electric bikes in elevators

1.
School of Computer and Artificial Intelligence, Hefei Normal University, Hefei 230601, China
2.
School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China
3.
State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China

Received: 05 March 2025 Revised: 21 May 2025 Accepted: 05 June 2025 Published: 12 June 2025

The presence of electric bicycles in elevators poses serious safety hazards, necessitating reliable automatic detection solutions. To address these issues, this paper proposes EBike-YOLO, an enhanced real-time detection model based on YOLOv10n, specifically optimized for elevator environments. EBike-YOLO introduces three key innovations: 1) the PIoU2_NWD loss function, combining adaptive penalty mechanisms, anchor-quality-aware gradient adjustments, non-monotonic attention, and Wasserstein distance for significantly improved object localization; 2) the EnhancedPSA structure, refining self-attention and feed-forward network architectures to enhance feature extraction; and 3) the C2f_Calibration module, incorporating self-calibration operations to robustly manage diverse orientations and scales of electric bicycles. Extensive experiments on a custom elevator dataset demonstrate substantial improvements over the YOLOv10n baseline, achieving notable enhancements in precision (4.1%), recall (0.3%), F1 score (2.0%), and mAP@50 (2.23%). Further validations on standard benchmark datasets (VisDrone2019, VOC2007, VOC2012) confirm the model's strong generalization capability, underscoring its applicability beyond specific elevator scenarios. These results clearly highlight the effectiveness, novelty, and practical value of the proposed model for diverse real-world detection tasks.
- Electric bicycles,
- YOLOv10n,
- PIoU2_NWD,
- EnhancedPSA,
- C2f_Calibration,
- electric bicycle
Citation: Tong Li, Lanfang Lei, Zhong Wang, Peibei Shi, Zhize Wu. An efficient improved YOLOv10 algorithm for detecting electric bikes in elevators[J]. Electronic Research Archive, 2025, 33(6): 3673-3698. doi: 10.3934/era.2025163

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Abstract

The presence of electric bicycles in elevators poses serious safety hazards, necessitating reliable automatic detection solutions. To address these issues, this paper proposes EBike-YOLO, an enhanced real-time detection model based on YOLOv10n, specifically optimized for elevator environments. EBike-YOLO introduces three key innovations: 1) the PIoU2_NWD loss function, combining adaptive penalty mechanisms, anchor-quality-aware gradient adjustments, non-monotonic attention, and Wasserstein distance for significantly improved object localization; 2) the EnhancedPSA structure, refining self-attention and feed-forward network architectures to enhance feature extraction; and 3) the C2f_Calibration module, incorporating self-calibration operations to robustly manage diverse orientations and scales of electric bicycles. Extensive experiments on a custom elevator dataset demonstrate substantial improvements over the YOLOv10n baseline, achieving notable enhancements in precision (4.1%), recall (0.3%), F1 score (2.0%), and mAP@50 (2.23%). Further validations on standard benchmark datasets (VisDrone2019, VOC2007, VOC2012) confirm the model's strong generalization capability, underscoring its applicability beyond specific elevator scenarios. These results clearly highlight the effectiveness, novelty, and practical value of the proposed model for diverse real-world detection tasks.

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