Automatic visual defects inspection of wind turbine blades via YOLO-based small object detection approach

Qiu, Zifeng; Wang, Shuangxin; Zhao-xi, Zeng; Yu, Dingli

doi:10.1117/1.jei.28.4.043023

Cited by 57 publications

(32 citation statements)

References 17 publications

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“…In 2019, Qiu et al [ 32 ] designed a WTB detection system, YSODA, which is based on the YOLOv3 algorithm. Qiu et al modified the YOLO architecture to support the multi-scale feature pyramid in the layers of CNN.…”

Section: Related Methodsmentioning

confidence: 99%

“…Datasets D1–D3 were created using different combinations of image augmentation techniques (e.g., flipping, rotation, and greyscale) can enhance the detection performance of DL methods [ 30 , 32 , 33 ]. Influenced by literature, three combinations of augmentation techniques were devised and applied to the original dataset (D0) to create three new datasets (Dataset 1 (D1), Dataset 2 (D2), and Dataset 3 (D3)) as shown in Table 3 .…”

Section: Methodsmentioning

confidence: 99%

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Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

2021

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Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

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Section: Related Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

2021

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“…A plethora of real-world applications is heavily dependent on accurate small object detection such as industrial (Qiu et al, 2019), agriculture (Wang et al, 2020;Zhang et al, 2020), sports (Xu et al, 2018), or security (Abe et al, 2008) applications. Small object detection is of particular interest for earth observation purposes such as the case of object detection in optical airborne (Wang et al, 2018), or satellite (Ren et al, 2018;Zhang et al, 2019) very high resolution data.…”

Section: Small Object Detectionmentioning

confidence: 99%

Scale-Awareness for More Accurate Object Detection Using Modified Single Shot Detectors

Tsironis¹,

Stentoumis²,

Lekkas³

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Object detection performance is directly related to the apparent size of the object to be detected, thus most state-of-the-art algorithms dedicate different detection heads for each object size. In this work, we propose an end-to-end pipeline to adapt a single-shot object detector (SSD) to the underlying object size distribution of the target detection domain. Our contributions are the adjustments to the detector architecture and the introduction of a novel batch sampling method. To validate the effect of our method, we chose a task-specific highly specialized object detection and classification dataset of tomato fruits that apart from bounding box information, it also contains class information for three ripening stages of each tomato fruit.More specifically, the major motivation and contributions are discussed in relation to the recent bibliography. Next, an extensive analysis of our pipeline is presented, where the concept of scale alignment is thoroughly presented along with the novel sampling method. Following the results of a series of experiments, we conclude that our pipeline significantly improves over the “off-the-shelf” base single-shot detector and its detection performance is comparable to more elaborate algorithms, especially if we measure detection performance slightly disregarding box localization. Lastly, we include a stratified ablation study in the closing sections where we measure the impact of each step along our proposed SSD adaptation pipeline.

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“…A real-time YOLO-based face detector, YOLO-face, is presented by Chen et al [23]. Furthermore, YOLO is also widely employed for small object detection tasks [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

Quoc

Hoang

2021

Complexity

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Biometric traits gradually proved their importance in real-life applications, especially in identification field. Among the available biometric traits, the unique shape of the human ear has also received loads of attention from scientists through the years. Hence, numerous ear-based approaches have been proposed with promising performance. With these methods, plenty problems can be solve by the distinctiveness of ear features, such as recognizing human with mask or diagnose ear-related diseases. As a complete identification system requires an effective detector for real-time application, and the current richness and variety of ear detection algorithms are poor due to the small and complex shape of human ears. In this paper, we introduce a new human ear detection pipeline based on the YOLOv3 detector. A well-known face detector named RetinaFace is also added in the detection system to narrow the regions of interest and enhance the accuracy. The proposed method is evaluated on an unconstrained dataset, which shows its effectiveness.

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Automatic visual defects inspection of wind turbine blades via YOLO-based small object detection approach

Cited by 57 publications

References 17 publications

Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

Scale-Awareness for More Accurate Object Detection Using Modified Single Shot Detectors

Real‐Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

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