AIE-YOLO: Auxiliary Information Enhanced YOLO for Small Object Detection

Yan, Bingnan; Li, Jiaxin; Yang, Zeyu; Zhang, Xinpeng; Hao, Xiaolong

doi:10.3390/s22218221

Cited by 21 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine vision has become a crucial research area in the field of autonomous driving. In object detection algorithms, the YOLOv5s algorithm utilizes convolutional neural networks to calculate the positions of objects to be recognized 22 – 24 , classifying and localizing them accurately. YOLOv5s is a high-accuracy neural network that surpasses the limitations of traditional image processing algorithms.…”

Section: Methodsmentioning

confidence: 99%

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Qiu,

Tang,

Yang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The resolution of traffic congestion and personal safety issues holds paramount importance for human’s life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm’s backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.

show abstract

Section: Methodsmentioning

confidence: 99%

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Qiu,

Tang,

Yang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Furthermore, they made improvements to cluster center distance and loss function parameters, achieving a mAP of 92.03%. Yan et al [30] proposed a method based on YOLOv5 that incorporates multiple-scale receptive fields to capture image information and introduces attention branches to enhance feature expression, thereby addressing the issue of small target features, like pedestrians, tending to disappear. Dewi et al [31] trained enhanced datasets for traffic sign detection using generative adversarial networks, achieving a mAP of 84.9% for YOLOv3 and 89.33% for YOLOv4.…”

Section: Related Workmentioning

confidence: 99%

Research on multitask model of object detection and road segmentation in unstructured road scenes

Gao,

Zhao,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

With the rapid development of artificial intelligence and computer vision technology, autonomous driving technology has become a hot area of concern. The driving scenarios of autonomous vehicles can be divided into structured scenarios and unstructured scenarios.Compared with structured scenes, unstructured road scenes lack the constraints of lane lines and traffic rules, and the safety awareness of traffic participants is weaker. Therefore, there are new and higher requirements for the environment perception tasks of autonomous vehicles in unstructured road scenes. The current research rarely integrates the target detection and road segmentation to achieve the simultaneous processing of target detection and road segmentation of autonomous vehicle in unstructured road scenes. Aiming at the above issues, a multitask model for object detection and road segmentation in unstructured road scenes is proposed. Through the sharing and fusion of the object detection model and road segmentation model, multitask model can complete the tasks of multi-object detection and road segmentation in unstructured road scenes while inputting a picture. Firstly, MobileNetV2 is used to replace the backbone network of YOLOv5, and multi-scale feature fusion is used to realize the information exchange layer between different features.Subsequently, a road segmentation model was designed based on the DeepLabV3+ algorithm.Its main feature is that it uses MobileNetV2 as the backbone network and combines the binary classification focus Loss function for network optimization. Then, we fused the object detection algorithm and road segmentation algorithm based on the shared MobileNetV2 network to obtain a multitask model and trained it on both the public dataset and the self-built dataset NJFU. The training results demonstrate that the multitask model significantly enhances the algorithm's execution speed by approximately 10 FPS while maintaining the accuracy of object detection and road segmentation. Finally, we conducted validation of the multitask model on an actual vehicle.

show abstract

“…Wang et al [18] combined the small object detection layer with YOLOv4 network for traffic sign detection. Yan et al [19] combined attention with YOLOv5 and proposed a new model for traffic sign detection. These methods have inspired us tremendously.…”

Section: Yolov5smentioning

confidence: 99%

TRD-YOLO: A Real-Time, High-Performance Small Traffic Sign Detection Algorithm

Chu

Zhang

Yan

et al. 2023

Sensors

View full text Add to dashboard Cite

Traffic sign detection is an important part of environment-aware technology and has great potential in the field of intelligent transportation. In recent years, deep learning has been widely used in the field of traffic sign detection, achieving excellent performance. Due to the complex traffic environment, recognizing and detecting traffic signs is still a challenging project. In this paper, a model with global feature extraction capabilities and a multi-branch lightweight detection head is proposed to increase the detection accuracy of small traffic signs. First, a global feature extraction module is proposed to enhance the ability of extracting features and capturing the correlation within the features through self-attention mechanism. Second, a new, lightweight parallel decoupled detection head is proposed to suppress redundant features and separate the output of the regression task from the classification task. Finally, we employ a series of data enhancements to enrich the context of the dataset and improve the robustness of the network. We conducted a large number of experiments to verify the effectiveness of the proposed algorithm. The accuracy of the proposed algorithm is 86.3%, the recall is 82.1%, the mAP@0.5 is 86.5% and the mAP@0.5:0.95 is 65.6% in TT100K dataset, while the number of frames transmitted per second is stable at 73, which meets the requirement of real-time detection.

show abstract

AIE-YOLO: Auxiliary Information Enhanced YOLO for Small Object Detection

Cited by 21 publications

References 39 publications

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Research on multitask model of object detection and road segmentation in unstructured road scenes

TRD-YOLO: A Real-Time, High-Performance Small Traffic Sign Detection Algorithm

Contact Info

Product

Resources

About