Aerial Image Object Detection Based on Superpixel-Related Patch

Lin, Jiehua; Zhao, Yan; Wang, Shigang; Chen, Meimei; Lin, Hai; Qian, Zhihong

doi:10.1007/978-3-030-87355-4_22

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The basic architecture of YOLOv5s can be divided into four parts: input, backbone, neck, and prediction. 20 Input is the sample input.…”

Section: Methodsmentioning

confidence: 99%

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Color-patterned fabric defect detection based on the improved YOLOv5s model

Wang

et al. 2023

Textile Research Journal

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The complex background pattern of color-patterned fabrics, the small target of some defects, the difficulty separating them from the background, and the extreme aspect ratio present challenges for their automated, real-time detection. To solve the above problems, the YOLOv5s-based color-patterned fabric defect detection algorithm was proposed by combining lightweight modules. For the small target defects of color-patterned fabrics, coordinate attention was introduced in the feature extraction part to guide the model to focus entirely on the target defect area and suppress the background noise of color-patterned fabrics. Meanwhile, the bidirectional feature pyramid network was introduced in the feature fusion part to give different fusion weights to the extracted feature maps, improve the efficiency of feature fusion, and guide the model further to distinguish the fabric defects from the color-patterned background. Finally, the k-means algorithm was used to generate anchor boxes for the extreme aspect ratio of fabric defects to improve the training efficiency and accuracy of the model. Self-built datasets were experimented with to verify the improved model’s detection effect. The results show that the improved YOLOv5s model can reach 87.7% and 0.881 in mean average precision and F1 score, which are 2.3% and 0.02 better than the original model, respectively. The detection speed of the improved YOLOv5s model reached 60.24 frames per second (GPU 1660). After deployment on the fabric defect detection platform, the speed of detecting color-patterned fabrics can reach 15 m/min.

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“…The basic architecture of YOLOv5s can be divided into four parts: input, backbone, neck, and prediction. 20 Input is the sample input.…”

Section: Methodsmentioning

confidence: 99%

“…The basic architecture of YOLOv5s can be divided into four parts: input, backbone, neck, and prediction. 20 Input is the sample input. The backbone consists of five C3 downsampling modules and the fast spatial pyramid pooling-fast (SPPF) layer to extract multi-scale features from the sample.…”

Section: Methodsmentioning

confidence: 99%

Color-patterned fabric defect detection based on the improved YOLOv5s model

Wang

et al. 2023

Textile Research Journal

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Efficient Aerial Images Algorithms over Multi-objects labeling and Semantic Segmentation

Naseer,

Jalal

2024

2024 5th International Conference on Advancements in Computational Sciences (ICACS)

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Spherical Superpixel Segmentation with Context Identity and Contour Intensity

Liao,

Guo,

et al. 2024

Symmetry

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Superpixel segmentation is a popular preprocessing tool in the field of image processing. Nevertheless, conventional planar superpixel generation algorithms are inadequately suited for segmenting symmetrical spherical images due to the distinctive geometric differences. In this paper, we present a novel superpixel algorithm termed context identity and contour intensity (CICI) that is specifically tailored for spherical scene segmentation. By defining a neighborhood range and regional context identity, we propose a symmetrical spherical seed-sampling method to optimize both the quantity and distribution of seeds, achieving evenly distributed seeds across the panoramic surface. Additionally, we integrate the contour prior to superpixel correlation measurements, which could significantly enhance boundary adherence across different scales. By implementing the two-fold optimizations on the non-iterative clustering framework, we achieve synergistic CICI to generate higher-quality superpixels. Extensive experiments on the public dataset confirm that our work outperforms the baselines and achieves comparable results with state-of-the-art superpixel algorithms in terms of several quantitative metrics.

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Aerial Image Object Detection Based on Superpixel-Related Patch

Cited by 3 publications

References 11 publications

Color-patterned fabric defect detection based on the improved YOLOv5s model

Color-patterned fabric defect detection based on the improved YOLOv5s model

Efficient Aerial Images Algorithms over Multi-objects labeling and Semantic Segmentation

Spherical Superpixel Segmentation with Context Identity and Contour Intensity

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