Improved Chunked Image Annotation Method for Electric Bikes in Complex Elevator Scenarios Based on Local Features

Summary

A chunked image annotation method based on local features is presented to improve electric bike detection in complex elevator scenarios using the EBike-DET dataset and mainstream object detection models.

Abstract

The increasing use of electric bikes (EBikes) in confined environments such as residential elevators has raised serious safety concerns and introduced considerable challenges for automated object detection, particularly due to frequent occlusions. Traditional detection approaches, which rely primarily on holistic annotations, often fail to accurately recognize partially occluded EBikes in visually complex scenes. To overcome these limitations, this study proposes a novel chunked annotation method based on local features, offering a more interpretable annotation strategy. By decomposing an EBike into multiple key regions for independent labeling, the proposed method enables detection models to learn fine-grained structural information, thereby improving robustness in occlusion-heavy conditions. Additionally, a dedicated dataset, EBike-DET, has been developed to support detection tasks in realistic elevator scenarios. Annotated using the chunked approach and augmented with simulated environmental conditions, the dataset enhances both model performance and adaptability. The proposed method promotes the development of explainable artificial intelligence (XAI) by making object detection more transparent and structurally interpretable, which is especially valuable in safety-critical applications. Extensive experiments are conducted using three mainstream models (YOLOv5, YOLOv10, and SSD). Results show that YOLOv5, when trained on EBike-DET with chunked annotations, achieves improvements of 3.7% in precision, 5.3% in recall, 4.5% in F1 score, and 4.4% in mAP. Compared to public datasets, EBike-DET demonstrates greater stability and robustness under occlusion. This study not only advances detection accuracy but also provides a step toward more interpretable and explainable AI solutions for deployment in real-world safety monitoring systems.

Introduction

With the rapid proliferation of electric bikes (EBikes) worldwide, particularly in China, where the total exceeded 350 million units by 2022, EBikes have become a dominant mode of short-distance transportation. However, their frequent use in confined spaces such as residential elevators introduces serious safety risks, including abnormal vibrations, equipment damage, unpleasant odors, and fire hazards. A recent study estimates that EBike-related fire incidents occur with a probability of approximately 1.44%¹. These risks highlight the urgent need for efficient and accurate EBike detection methods to enhance safety in elevator environments.

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Protocol

The EBike-DET dataset used in this study consists of images collected by the authors through on-site photography in elevator, parking-lot, and street environments, as well as publicly available EBike images obtained from web-based platforms. All on-site image collection was conducted in non-private environments solely for safety-related technical research on EBike detection. Images do not intentionally target individuals, and any incidentally captured persons are non-identifiable due to distance, occlusion, back-facing views, or appropriate processing that removes facial features and other personal identifiers. Web-sourced images were obtained exclusively from platfor....

Results

Comparison of holistic annotation and chunked annotation on public dataset

The evaluation was conducted on a public dataset comprising 210 EBike images collected from open surveillance and traffic monitoring scenes, with diverse lighting conditions, EBike colors, and varying degrees of occlusion. Each image was annotated using both the holistic method (single bounding box) and the proposed chunked method (dividing EBikes into wheels, front area, and rear areas).

Discussion

Critical steps

A critical step in this protocol is the chunked annotation method based on local features, where EBikes are segmented into wheel, front, and rear areas. This division ensures that detection models can learn fine-grained representations, which proved essential in occlusion-heavy elevator environments. For instance, YOLOv5 trained with chunked annotations on the EBike-DET dataset improved its mAP@0.5 from 0.925 to 0.966, underscoring the necessity of accurate loc.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
h5py (SSD)	HDF Group	2.10.0
matplotlib (SSD)	Matplotlib Community	3.1.2
matplotlib (YOLOv10)	Matplotlib Community	3.9.0
matplotlib (YOLOv5)	Matplotlib Community	3.8.4
matplotlib (YOLOv5+SAHI)	Matplotlib Community	3.8.4
matplotlib (YOLOv8-Seg)	Matplotlib Community	3.9.0
numpy (SSD)	NumPy Community	1.17.0
numpy (YOLOv10)	NumPy Community	1.26.3
numpy (YOLOv5)	NumPy Community	1.26.4
numpy (YOLOv5+SAHI)	NumPy Community	1.26.4
numpy (YOLOv8-Seg)	NumPy Community	1.26.3
onnx (YOLOv10)	ONNX	1.14.0
onnx (YOLOv5)	ONNX	1.14.0
onnx (YOLOv5+SAHI)	ONNX	1.14.0
onnxruntime (YOLOv10)	Microsoft	1.15.1
onnxruntime (YOLOv5)	Microsoft	1.15.1
onnxruntime (YOLOv5+SAHI)	Microsoft	1.15.1
opencv-python (SSD)	OpenCV	4.1.2.30
opencv-python (YOLOv10)	OpenCV	4.9.0.80
opencv-python (YOLOv5)	OpenCV	4.9.0.80
opencv-python (YOLOv5+SAHI)	OpenCV	4.9.0.80
opencv-python (YOLOv8-Seg)	OpenCV	4.9.0.80
pandas (YOLOv10)	Pandas Community	2.2.2
pandas (YOLOv5)	Pandas Community	2.2.2
pandas (YOLOv5+SAHI)	Pandas Community	2.2.2
pandas (YOLOv8-Seg)	Pandas Community	2.2.2
Pillow (SSD)	Pillow Developers	8.2.0
Pillow (YOLOv10)	Pillow Developers	10.2.0
Pillow (YOLOv5)	Pillow Developers	8.5.0
Pillow (YOLOv5+SAHI)	Pillow Developers	8.5.0
Pillow (YOLOv8-Seg)	Pillow Developers	10.2.0
psutil (YOLOv10)	Psutil Developers	5.9.8
psutil (YOLOv5)	Psutil Developers	5.9.8
psutil (YOLOv5+SAHI)	Psutil Developers	5.9.8
pycocotools (YOLOv10)	COCO Consortium	2.0.7
pycocotools (YOLOv5)	COCO Consortium	2.0.7
pycocotools (YOLOv5+SAHI)	COCO Consortium	2.0.7
pycocotools (YOLOv8-Seg)	COCO Consortium	2.0.7
py-cpuinfo (YOLOv10)	Py-CPUInfo Developers	9.0.0
py-cpuinfo (YOLOv5)	Py-CPUInfo Developers	9.0.0
py-cpuinfo (YOLOv5+SAHI)	Py-CPUInfo Developers	9.0.0
PyYAML (YOLOv10)	PyYAML	6.0.1
PyYAML (YOLOv5)	PyYAML	6.0.1
PyYAML (YOLOv5+SAHI)	PyYAML	6.0.1
PyYAML (YOLOv8-Seg)	PyYAML	6.0.1
requests (SSD)	Python Requests	2.27.1
requests (YOLOv10)	Python Requests	2.32.3
requests (YOLOv5)	Python Requests	2.31.0
requests (YOLOv5+SAHI)	Python Requests	2.31.0
SAHI	SAHI Developers	0.3.4+
scipy (SSD)	SciPy Community	1.2.1
scipy (YOLOv10)	SciPy Community	1.13.0
scipy (YOLOv5)	SciPy Community	1.13.0
scipy (YOLOv5+SAHI)	SciPy Community	1.13.0
scipy (YOLOv8-Seg)	SciPy Community	1.13.0
seaborn (YOLOv10)	Seaborn Developers	0.13.2
seaborn (YOLOv5)	Seaborn Developers	0.13.2
seaborn (YOLOv5+SAHI)	Seaborn Developers	0.13.2
seaborn (YOLOv8-Seg)	Seaborn Developers	0.13.2
shapely (YOLOv5+SAHI)	Shapely Developers	2.0.4
SSD	Caffe/Original SSD Authors	Python 3.6.13+; PyTorch 1.2.0+; CUDA 10.0; CUDNN 7.4.1
tensorboard (SSD)	Google	2.10.1
tensorboard (YOLOv5)	Google	2.16.2
tensorboard (YOLOv5+SAHI)	Google	2.16.2
torchvision (SSD)	PyTorch	0.4.0
torchvision (YOLOv10)	PyTorch	0.15.2
torchvision (YOLOv5)	PyTorch	0.17.2
torchvision (YOLOv5+SAHI)	PyTorch	0.17.2
torchvision (YOLOv8-Seg)	PyTorch	0.16.1+
tqdm (SSD)	TQDM Developers	4.60.0
tqdm (YOLOv10)	TQDM Developers	4.66.4
tqdm (YOLOv5)	TQDM Developers	4.66.2
tqdm (YOLOv5+SAHI)	TQDM Developers	4.66.2
ultralytics (YOLOv8-Seg)	Ultralytics	8.2.99+
YOLOv10	YOLOv10 Team	Python 3.8.0+; PyTorch 2.0.1+cu118; CUDA 11.8; CUDNN 8.7
YOLOv5	Ultralytics	Python 3.8.0+; PyTorch 2.2.2+; CUDA 11.2; CUDNN 8.1.2
YOLOv5 + SAHI	Ultralytics + SAHI Developers	Python 3.8.0+; PyTorch 2.2.2+; CUDA 11.2; CUDNN 8.1.2
YOLOv8-Seg	Ultralytics	Python 3.8.0+; PyTorch 2.0.1+cu118; CUDA 11.8; CUDNN 8.6.0+