YOLO-Drone: An Optimized YOLOv8 Network for Tiny UAV Object Detection

Zhai, Xianxu; Huang, Zhihua; Li, Tao; Liu, Hanzheng; Wang, Siyuan

doi:10.3390/electronics12173664

Cited by 31 publications

(10 citation statements)

References 37 publications

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“…YOLOv8 has five models with different scales. They are YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x [38]. The network architecture of YOLOv8 consists of Input, Backbone, Head, and Detect.…”

Section: Object Detection Methodsmentioning

confidence: 99%

PGDS-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images

Yang,

Zhou,

Song

et al. 2023

Applied Sciences

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Recently, object detection has become a research hotspot in computer vision, which often detects regular images with small viewing angles. In order to obtain a field of view without blind spots, fisheye cameras, which have distortions and discontinuities, have come into use. The fisheye camera, which has a wide viewing angle, and an unmanned aerial vehicle equipped with a fisheye camera are used to obtain a field of view without blind spots. However, distorted and discontinuous objects appear in the captured fisheye images due to the unique viewing angle of fisheye cameras. It poses a significant challenge to some existing object detectors. To solve this problem, this paper proposes a PGDS-YOLOv8s model to solve the issue of detecting distorted and discontinuous objects in fisheye images. First, two novel downsampling modules are proposed. Among them, the Max Pooling and Ghost’s Downsampling (MPGD) module effectively extracts the essential feature information of distorted and discontinuous objects. The Average Pooling and Ghost’s Downsampling (APGD) module acquires rich global features and reduces the feature loss of distorted and discontinuous objects. In addition, the proposed C2fs module uses Squeeze-and-Excitation (SE) blocks to model the interdependence of the channels to acquire richer gradient flow information about the features. The C2fs module provides a better understanding of the contextual information in fisheye images. Subsequently, an SE block is added after the Spatial Pyramid Pooling Fast (SPPF), thus improving the model’s ability to capture features of distorted, discontinuous objects. Moreover, the UAV-360 dataset is created for object detection in fisheye images. Finally, experiments show that the proposed PGDS-YOLOv8s model on the VOC-360 dataset improves mAP@0.5 by 19.8% and mAP@0.5:0.95 by 27.5% compared to the original YOLOv8s model. In addition, the improved model on the UAV-360 dataset achieves 89.0% for mAP@0.5 and 60.5% for mAP@0.5:0.95. Furthermore, on the MS-COCO 2017 dataset, the PGDS-YOLOv8s model improved AP by 1.4%, AP50 by 1.7%, and AP75 by 1.2% compared with the original YOLOv8s model.

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Section: Object Detection Methodsmentioning

confidence: 99%

PGDS-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images

Yang,

Zhou,

Song

et al. 2023

Applied Sciences

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“…To address these challenges, this paper embeds the GAM in the CSPDarkNet backbone network. GAM [19] is a lightweight, practical, and straightforward component that seamlessly integrates into CNN architectures. The structure of the GAM is shown in figure 7.…”

Section: Gammentioning

confidence: 99%

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Ding,

Zhan,

et al. 2024

Meas. Sci. Technol.

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Surface defects in bearings not only affect the appearance but also impact the service life and performance. Therefore, it is imperative for bearing manufacturers to conduct quality inspections before bearings leave the factory. However, traditional visual inspection methods exhibit shortcomings such as high omission rates, insufficient feature fusion and oversized models when dealing with multiple target defects in bearings. To address these challenges, this paper proposes a surface defect detection method for bearings based on an improved Yolov8 algorithm (G-Yolov8). Firstly, a C3Ghost convolutional module based on the Ghost module is constructed in YOLOv8 to simplify model computational costs. Secondly, a Global Attention Mechanism (GAM) module is designed at the end of the backbone network to increase sensitivity to implicit small target area features and optimize feature extraction efficiency.Subsequently, a deep deformable convolution feature pyramid network (TDSFPN) is constructed by introducing the deformable convolutional networks version 2 (DCNv2) and the lightweight Content-Aware Reassembly of Features upsampling operator (CARAFE) to reduce sampling information loss and improve the fusion of multi-scale target defects. Finally, different attention mechanisms are embedded in the detection network to construct a Multi-Attention Detection Head (MADH) to replace the decoupled head, refining classification and localization tasks, reducing feature confusion, and improving the model's detection accuracy. Experimental results demonstrate that the improved algorithm achieves a 3.5% increase in mean average precision on a self-made small-scale train bearing surface defect dataset, with a 17.3% reduction in model size. This improvement not only enhances accuracy but also addresses the requirement for lightweight deployment in subsequent stages.

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“…YOLOv8 is composed of three parts: Backbone, FPN, and Yolo Head [23,24], as shown in Figure 2. The Backbone network uses CSPDarknet to extract the effective feature layer, and then realizes the feature fusion of the effective feature layer in FPN.…”

Section: Yolov8 Network Architecturementioning

confidence: 99%

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

Wang,

Huang

2024

Applied Sciences

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Urban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover serves as the hub connecting the underground pipe network with the ground, the generation of underground pipe network can be realized by obtaining the location and category information of the manhole cover. Therefore, this paper proposed a manhole cover detection method based on UAV aerial photography to obtain ground images, using image super-resolution reconstruction and image positioning and classification. Firstly, the urban image was obtained by UAV aerial photography, and then the YOLOv8 object detection technology was used to accurately locate the manhole cover. Next, the SRGAN network was used to perform super-resolution processing on the manhole cover text to improve the clarity of the recognition image. Finally, the clear manhole cover text image was input into the VGG16_BN network to realize the manhole cover classification. The experimental results showed that the manhole cover classification accuracy of this paper’s method reached 97.62%, which verified its effectiveness in manhole cover detection. The method significantly reduces the time and labor cost and provides a new method for manhole cover information acquisition.

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YOLO-Drone: An Optimized YOLOv8 Network for Tiny UAV Object Detection

Cited by 31 publications

References 37 publications

PGDS-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images

PGDS-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

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