Soybean in the field has a wide range of intermixed weed species and a complex distribution status, and the weed identification rate of traditional methods is low. Therefore, a weed identification method is proposed based on the optimized Faster R-CNN algorithm for the soybean seedling. Three types of weed datasets, including soybean, with a total of 9816 photos were constructed, and cell phone photo data were used for training and recognition. Firstly, by comparing the classification effects of ResNet50, VGG16, and VGG19, VGG19 was identified as the best backbone feature extraction network for model training. Secondly, an attention mechanism was embedded after the pooling layer in the second half of VGG19 to form the VGG19-CBAM structure, which solved the problem of low attention to the attention target during model training using the trained Faster R-CNN algorithm to identify soybean and weeds in the field under the natural environment and compared with two classical target detection algorithms, SSD and Yolov4. The experimental results show that the Faster R-CNN algorithm using VGG19-CBAM as the backbone feature extraction network can effectively identify soybeans and weeds in complex backgrounds. The average recognition speed for a single image is 336 ms, and the average recognition accuracy is 99.16%, which is 5.61% higher than before optimization, 2.24% higher than the SSD algorithm, and 1.24% higher than the Yolov4 algorithm. Therefore, this paper’s optimized target detection model is advantageous and can provide a scientific method for accurate identification and monitoring of grass damage.

/pdf/weed-identification-in-soybean-seedling-stage-based-on-e40og9ks.pdf

Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm

The increasing demand for wind power requires more frequent inspections to identify defects in the Wind Turbine Blades (WTBs). These defects, if not detected, can compromise the structural integrity and safety of wind turbines. As WTBs are crucial and costly components, they may suffer material degradation and fatigue failure, which affects their performance and safety. Thus, the urgency for efficient and regular monitoring to maintain their structural integrity is greater than ever. This review paper explores innovative methods in fatigue testing, damage detection, and structural reliability in WTBs, focusing on the use of recent inspection methods, including those that take advantage of drones. Drones are used to identify defects such as cracks, erosion, and coating irregularities using text high-resolution imagery with the onboard cameras. Various investigators have developed novel data-driven approaches, incorporating machine learning and deep learning, to accurately identify these defects. Although deep text learning-based image processing has been successful in other public infrastructure contexts, its application to wind turbine inspection from aerial images presents unique challenges. This paper also highlights the critical role of failure inspection in enhancing the operational integrity of WTBs, showcasing state-of-the-art deep learning techniques that are pivotal for identifying and analyzing failures in WTBs from images captured by drones. The paper provides insights into the latest developments in using drone imagery for blade defect detection, contrasting this method with traditional non-destructive techniques. This approach could significantly transform the wind energy industry by offering a more efficient, automated, and precise way of ensuring the structural health of wind turbines. Unlike previous studies that predominantly focus on isolated aspects such as inspection or fatigue, this review paper not only integrates the three major aspects of WTBs integrity in terms of aerial inspection, image processing using machine learning, and structural integrity of the blade but also undertakes an extensive examination of the prevailing methodologies in the field, pinpointing crucial gaps and challenges. It provides a detailed review of existing research, covering various areas including automated inspection, image processing techniques, fatigue analysis, and the reliability of wind turbines. This approach enriches the discourse by offering a multifaceted perspective on WTB maintenance, thereby advancing the understanding of operational integrity within the field of wind energy.

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Ship speed extraction (SSE) in busy water based on an automatic identification system (AIS) has received widespread attention due to its high efficiency. However, this method is usually degraded by data delay and loss. In this article, we propose a novel SSE method based on machine vision and unmanned aerial vehicle (UAV) video to measure ship speed and repair AIS data in busy water. Our framework integrates YOLO v4 into simple online and real-time tracking with a deep association metric (Deep SORT) to detect and track ship targets. Specifically, we design a tracking buffer to promote the insensitivity of the tracker under uncertain environmental changes and filter the tracker for measuring speed. Furthermore, a motion vector computation term is proposed based on sparse optical flow and a box diagram. After using camera calibration to convert the motion vector to displacement in the real world, the SSE results are obtained. Exhaustive experiments are conducted on various scenarios, i.e., illumination and ship distribution, from our ship’s remote dataset. The results verify that the proposed framework performs excellently, with an average speed measurement accuracy above 95% in complex waters, and the AIS data repair effect is remarkable. Moreover, it is also proven that machine vision technology can assist AIS information in supervising complex waters and ensuring navigation safety, which paves the way for extracting ship traffic flow information.

Multiship Speed Measurement Method Based on Machine Vision and Drone Images

Since wind turbines are exposed to harsh working environments and variable weather conditions, wind turbine blade condition monitoring is critical to prevent unscheduled downtime and loss. Realizing that common convolutional neural networks are difficult to use in embedded devices, a lightweight convolutional neural network for wind turbine blades (WTBMobileNet) based on spectrograms is proposed, reducing computation and size with a high accuracy. Compared to baseline models, WTBMobileNet without data augmentation has an accuracy of 97.05%, a parameter of 0.315 million, and a computation of 0.423 giga floating point operations (GFLOPs), which is 9.4 times smaller and 2.7 times less computation than the best-performing model with only a 1.68% decrease in accuracy. Then, the impact of difference data augmentation is analyzed. The WTBMobileNet with augmentation has an accuracy of 98.1%, and the accuracy of each category is above 95%. Furthermore, the interpretability and transparency of WTBMobileNet are demonstrated through class activation mapping for reliable deployment. Finally, WTBMobileNet is explored in drones image classification and spectrogram object detection, whose accuracy and mAP@[0.5, 0.95] are 89.55% and 70.7%, respectively. This proves that WTBMobileNet not only has a good performance in spectrogram classification, but also has good application potential in drone image classification and spectrogram object detection.

A Lightweight CNN for Wind Turbine Blade Defect Detection Based on Spectrograms

Ship speed extraction (SSE) in busy water based on an automatic identification system (AIS) has received widespread attention due to its high efficiency. However, this method is usually degraded by data delay and loss. In this paper, we propose a novel SSE method based on machine vision and unmanned aerial vehicle (UAV) video to measure ship speed and repair AIS data in busy water. Our framework integrates YOLO v4 into simple online and real-time tracking with a deep association metric (Deep SORT) to detect and track ship targets. Specifically, we design a tracking buffer to promote the insensitivity of the tracker under uncertain environmental changes and filter the tracker for measuring speed. Furthermore, a motion vector computation term is proposed based on sparse optical flow and a box diagram. After using camera calibration to convert the motion vector to displacement in the real world, the SSE results are obtained. Exhaustive experiments are conducted on various scenarios, i.e., illumination and ship distribution, from our ship’s remote dataset. The results verify that the proposed framework performs excellently, with an average speed measurement accuracy above 95% in complex waters, and the AIS data repair effect is remarkable. Moreover, it is also proven that machine vision technology can assist AIS information in supervising complex waters and ensuring navigation safety, which paves the way for extracting ship traffic flow information. 

Multi-ship Speed Measurement Method Based on Machine Vision and Drone Images

Improved YOLOv4 algorithm for safety management of on-site power system work

In the field of wind turbine surface defect detection, most existing defect detection algorithms have a single solution with poor generalization to the dilemma of insufficient defect samples and have unsatisfactory precision for small and concealed defects. Inspired by meta-learning ideology, we devised a cross-task training strategy. By exploring the common properties between tasks, the hypothesis space shrinks so that the needed sample size that satisfies a reliable empirical risk minimizer is reduced. To improve the training efficiency, a depth metric-based classification method is specially designed to find a sample-matching feature space with a good similarity measure by cosine distance. Additionally, a real-time feedback session is innovatively added to the model training loop, which performs information enhancement and filtering according to the task relevance. With dynamic activation mapping, it alleviates the information loss during traditional pooling operations, thus helping to avoid the missed detection of small-scale targets. Experimental results show that the proposed method has significantly improved the defect recognition ability under few-shot training conditions.

/pdf/dmnet-a-new-few-shot-framework-for-wind-turbine-surface-2jada41j.pdf

DMnet: A New Few-Shot Framework for Wind Turbine Surface Defect Detection

Measurement of Br(n,γ) cross sections up to stellar s-process temperatures at the CSNS Back-n

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Improved YOLOv4 algorithm for safety management of on-site power system work

DMnet: A New Few-Shot Framework for Wind Turbine Surface Defect Detection

Measurement of Br(n,γ) cross sections up to stellar s-process temperatures at the CSNS Back-n