A review of occluded objects detection in real complex scenarios for autonomous driving

Ruan, Jiageng; Cui, Hanghang; Huang, Yuhan; Li, Tongyang; Wu, Changcheng; Zhang, Kaixuan

doi:10.1016/j.geits.2023.100092

Cited by 12 publications

(2 citation statements)

References 68 publications

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“…Although the RCNN and YOLO series algorithms have clear advantages in object detection, they face challenges when dealing with small targets and occlusions. The RCNN [9] series employs a method of generating numerous region proposals to identify targets. However, this approach can result in a reduction in detection performance in scenarios involving small targets or complex occlusions [10], primarily due to the imprecision of these proposals.…”

Section: Introductionmentioning

confidence: 99%

Track fastener detection in special scenarios based on TSR-Net

Bai,

Duan,

et al. 2024

Eng. Res. Express

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The traditional method of detecting track fasteners can lead to erroneous results due to the presence of rail bolts, wires, and extraneous objects such as stones. Consequently, the study of fastener detection in specific environments is essential. To address these issues, the TSR-Net target detection model is introduced, which employs an efficient vision transformer as a backbone to increase the speed of feature extraction. The global image perception of the network is enhanced by integrating translation convolutions and inverted residual blocks within the neck network. A detection head with a self-supervised equivariant attention mechanism is designed to deal with the occlusion challenges of small fastener targets. The model is implemented on edge AI computing devices. Technical validation indicates that TSR-Net achieves 94.2% detection precision and operates at 47 frames per second, thereby enabling accurate, real-time detection of small-target occlusion fasteners.

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Section: Introductionmentioning

confidence: 99%

Track fastener detection in special scenarios based on TSR-Net

Bai,

Duan,

et al. 2024

Eng. Res. Express

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“…Object detection in complex environments is an extremely important and challenging task within the realm of computer vision [1]. In such intricate settings, factors such as variations in lighting angles and intensity at different distances and positions, coupled with the diversity in observers' viewpoints, observational angles, and distances, can induce intricate changes in brightness, shadows, contrast, position, and posture between the background and the target objects [2]. These elements contribute to low precision and poor timeliness in detecting and recognizing occluded targets under complex conditions, hindering the accurate interpretation of real-life scenarios.…”

Section: Introductionmentioning

confidence: 99%

Complex Scene Occluded Object Detection with Fusion of Mixed Local Channel Attention and Multi-Detection Layer Anchor-Free Optimization

Su,

2024

Automation

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The field of object detection has widespread applicability in many areas. Despite the multitude of object detection methods that are already established, complex scenes with occlusions still prove challenging due to the loss of information and dynamic changes that reduce the distinguishable features between the target and its background, resulting in lower detection accuracy. Addressing the shortcomings in detecting obscured objects in complex scenes with existing models, a novel approach has been proposed on the YOLOv8n architecture. First, the enhancement begins with the addition of a small object detection head atop the YOLOv8n architecture to keenly detect and pinpoint small objects. Then, a blended mixed local channel attention mechanism is integrated within YOLOv8n, which leverages the visible segment features of the target to refine the feature extraction hampered by occlusion impacts. Subsequently, Soft-NMS is introduced to optimize the candidate bounding boxes, solving the issue of missed detection under overlapping similar targets. Lastly, using universal object detection evaluation metrics, a series of ablation experiments on public datasets (CityPersons) were conducted alongside comparison trials with other models, followed by testing on various datasets. The results showed an average precision (map@0.5) reaching 0.676, marking a 6.7% improvement over the official YOLOv8 under identical experimental conditions, a 7.9% increase compared to Gold-YOLO, and a 7.1% rise over RTDETR, also demonstrating commendable performance across other datasets. Although the computational load increased with the addition of detection layers, the frames per second (FPS) still reached 192, which meets the real-time requirements for the vast majority of scenarios. Such findings indicate that the refined method not only significantly enhances performance on occluded datasets but can also be transferred to other models to boost their performance capabilities.

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Enhancing Nighttime Vehicle Segmentation for Autonomous Driving Based on YOLOv5

Huang

2024

Communications in Computer and Information Science

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A review of occluded objects detection in real complex scenarios for autonomous driving

Cited by 12 publications

References 68 publications

Track fastener detection in special scenarios based on TSR-Net

Track fastener detection in special scenarios based on TSR-Net

Complex Scene Occluded Object Detection with Fusion of Mixed Local Channel Attention and Multi-Detection Layer Anchor-Free Optimization

Enhancing Nighttime Vehicle Segmentation for Autonomous Driving Based on YOLOv5

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