Defect Identification of Adhesive Structure Based on DCGAN and YOLOv5

Jin, Yingji; Gao, Huifang; Fan, Xiaoliang; Khan, Hassan; Chen, Youxing

doi:10.1109/access.2022.3193775

Cited by 14 publications

(5 citation statements)

References 18 publications

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“…As the best algorithm of the YOLO family, YOLv5 has a slow detection speed, and there are problems involving the false detection of leaks in large objects, which cannot be detected well. Therefore, research scholars have started to study the improved YOLOv5 algorithm, and the main improvement methods are Transformers [20][21][22][23], attention mechanisms [24][25][26][27], etc.…”

Section: Related Workmentioning

confidence: 99%

Study of a Machine Vision Approach to Leak Monitoring of a Marine System

Jiang

Dai

Zhang

et al. 2023

JMSE

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Leak monitoring is essential for the intelligent operation and maintenance of marine systems, and can effectively prevent catastrophic accidents on ships. In response to this challenge, a machine vision-based leak model is proposed in this study and applied to leak detection in different types of marine system in complex engine room environments. Firstly, an image-based leak database is established, and image enhancement and expansion methods are applied to the images. Then, Standard Convolution and Fast Spatial Pyramid Pooling modules are added to the YOLOv5 backbone network to reduce the floating-point operations involved in the leak feature channel fusion process, thereby improving the detection speed. Additionally, Bottleneck Transformer and Shuffle Attention modules are introduced to the backbone and neck networks, respectively, to enhance the feature representation performance, select critical information for the leak detection task, and suppress non-critical information to improve detection accuracy. Finally, the proposed model’s effectiveness is verified using leak images collected by the ship’s video system. The test results demonstrate that the proposed model exhibits excellent recognition performance for various types of leak, especially for drop-type leaks (for which the accuracy reaches 0.97).

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Section: Related Workmentioning

confidence: 99%

Study of a Machine Vision Approach to Leak Monitoring of a Marine System

Jiang

Dai

Zhang

et al. 2023

JMSE

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“…They used the CBAM attention mechanism to handle dense object scenarios. According to the experimental results, the detection performance of TPH-YOLO v5 improved by about 7% compared to the original performance of YOLO v5 [29] to improve YOLO v5 for highquality defect detection. The authors refined the loss function and the DCGAN structure to generate high-quality defects.…”

Section: Introductionmentioning

confidence: 96%

Automatic detection and counting of fisheries using fish images

Tall,

Ngom,

Sadio

et al. 2023

Bull.Socinf.The.App

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In Senegal, stock recovery and fish classification are based on manual data collection, and the fish caught by the fishery are not often declared. What's more, data collection suffers from a lack of tools for monitoring and counting fish caught at fishing docks. Researchers have carried out studies on the fishery in Senegal, but data collection is almost non-existent. Moreover, there is no local fisheries database or automatic detection and counting algorithm. In this paper, a semantic segmentation algorithm is proposed using intelligent systems for the collection of fishery catches, for the formation of the local database. The data are collected by taking images of fish at the Soumbédioune fishing wharf in Senegal, and are completed with the Fishbase database. They were applied to the algorithm and resulted in a segmented dataset with masks. This constitutes our local database. The database is used with YOLO v8. The latter is very important for detecting images with bounding boxes in order to train the model. The results obtained are very promising for the proposed automatic poison detection and counting model. For example, the recall-confidence scores translate into bounding box performance with scores ranging from 0.01 to 0.75, confirming the performance of this model with bounding boxes

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“…Experimental results reveal a noteworthy enhancement in the detection performance of TPH-YOLO v5, showing approximately a 7% improvement compared to the original performance of YOLO v5. In a separate study, [33] enhances YOLO v5 for high-quality defect detection. This involves refining both the loss function and the DCGAN structure to generate defects of superior quality.…”

Section: Introductionmentioning

confidence: 99%

Fishery Detection and Counting Model uing Fish Images

TALL,

NGOM,

SADIO

et al. 2024

Preprint

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Fishing in Senegal is threatened by overfishing, compounded by a lack of fisheries management. Stock replenishment and fish classification are generally done manually, and catches are not always declared. In addition, the collection of fishing data suffers from a lack of tools for monitoring and counting the fish caught at the wharves. Although researchers have carried out studies on the fishery in Senegal, data collection is virtually non-existent, and there is no local database dedicated to the fishery or automatic detection and counting algorithm. In this article, a model for automatic detection and counting of fished species is proposed, using a semantic segmentation algorithm. The data used to form the adapted local database are collected from fish images taken at the Soumbédioune fishing wharf in Senegal. This database is supplemented by the Fishbase. This collected data is then segmented to form the appropriate local database. This database is used in conjunction with YOLO v8, an essential element in the detection of images with bounding boxes, to train the model. The results obtained are very promising for the proposed automatic fish detection and counting model. For example, the recall-confidence scores reflect the performance of bounding boxes, with scores ranging from 0.01 to 0.75, thus confirming the effectiveness of the model with bounding boxes. These results are of great importance for improving fisheries policies in Senegal.

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Defect Identification of Adhesive Structure Based on DCGAN and YOLOv5

Cited by 14 publications

References 18 publications

Study of a Machine Vision Approach to Leak Monitoring of a Marine System

Study of a Machine Vision Approach to Leak Monitoring of a Marine System

Automatic detection and counting of fisheries using fish images

Fishery Detection and Counting Model uing Fish Images

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