Bidirectional YOLO: improved YOLO for foreign object debris detection on airport runways

Ren, Maiyu; Wan, Weibing; Yu, Zedong; Zhao, Y. M.

doi:10.1117/1.jei.31.6.063047

Cited by 8 publications

(2 citation statements)

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“…Tu et al 33 suggested a new YOLOv3 network to recognize heavy truck blind regions in real-time for pedestrian safety. Ren et al 34 introduced Bi-YOLO to enhance the detection accuracy of tiny airport debris and mitigate the negative impact of low-light environments during nighttime detection. As a result, YOLO has found widespread use across various fields and has become an essential tool.…”

Section: Yolomentioning

confidence: 99%

AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection

Yun

Zhao

2023

J. Electron. Imag.

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As the issue of garbage pollution becomes increasingly urgent, accurate and effective garbage detection can play a crucial role in minimizing environmental pollution and facilitating the conversion of waste into energy. However, the accuracy of current waste detection algorithms is limited. We propose an approach called the adaptive activate decoupled head for YOLOv5 (AADH-YOLOv5) for detecting garbage. Our model improves localization accuracy without significantly increasing floating point of operations (FLOPs) or parameters. Extensive testing on the newly created domestic garbage dataset-6 demonstrates that AADH-YOLOv5 outperforms YOLOv5l and Cascade R-CNN, achieving a 5.4% and 4.2% improvement in mAP@0.5, respectively. Our study also contributes to the field by providing a new dataset for garbage detection research.

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Section: Yolomentioning

confidence: 99%

AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection

Yun

Zhao

2023

J. Electron. Imag.

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“…Wan et al [20] proposed Shuffle-ECANet to extract the features of road damages. Ren et al [21], Liu et al [22] and Wan et al exploited BiFPN to optimize the feature fusion of foreign objects on airport runways, faces and road damages, respectively. Yan et al [23] introduced wavelet transform into PANet to deal with the feature loss problem of traffic signs after multiple downsampling and pooling operations.…”

Section: Introductionmentioning

confidence: 99%

YOLOv5-FF: Detecting Floating Objects on the Surface of Fresh Water Environments

et al. 2023

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Real-time detection and timely treatment of floating objects on rivers, lakes and reservoirs is very essential to protect water environment and maintain the safety of navigation and water projects. YOLOv5, as a one-stage object detection solution, is very suitable for real-time floating object detection. However, it suffers from the problem of the false detection and missed detection of floating objects especially of small floating objects. In this paper, we conducts a series of improvements on YOLOv5 to alleviate the problem. Concretely, we propose a hybrid attention mechanism supporting the interaction among channels over a long distance while preserving the direct correspondence between channels and their weights. Base on the attention mechanism, we propose an adaptive feature extraction module to capture the feature information of objects in the case of the feature loss caused by downsampling operations. Based on the attention mechanism and dilated encoder, we construct a feature expression enhancement module to cover large objects while not losing small objects in the same certain scale range. We also add a detection layer for small objects to improve the performance in detecting small floating objects. The experiments on the data set verify the usefulness and effectiveness of our work.

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FF-Yolo: A Feature-Fusion Yolo Model for Small Scale FODs Detection in Airport Runways

Biswas,

Ganesh

2024

Lecture Notes in Computer Science

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Bidirectional YOLO: improved YOLO for foreign object debris detection on airport runways

Cited by 8 publications

References 0 publications

AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection

AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection

YOLOv5-FF: Detecting Floating Objects on the Surface of Fresh Water Environments

FF-Yolo: A Feature-Fusion Yolo Model for Small Scale FODs Detection in Airport Runways

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