•This study tackles the crucial issue of insulator defect detection in power grid systems.•It compares YOLO v7 and YOLO v8 for detecting broken insulators and flashover damaged insulators.•Using a ...real-world dataset, the research shows YOLO v8 achieves a superior rate of 98.99 percent accuracy.
Insulators are crucial components of power grid systems, safeguarding against electrical conductor breaks. However, their prolonged exposure to complex outdoor environments renders them susceptible to defects. In this study, we address the importance of accurate insulator defect detection and propose an approach using the You Only Look Once (YOLO) object detection framework. In particular, we compare the performance of YOLO v8 against YOLO v7 in detecting two specific types of insulator defects—broken insulators and flashover damaged insulators. Leveraging the Insulator Defect Image Dataset, our results demonstrate that YOLO v8 achieves superior accuracy with a rate of 98.99 percent along with a mean average precision (mAP) of 99.10 percent. The findings underscore the efficacy of YOLO v8 in improving the reliability and resilience of power grid systems by allowing timely and accurate detection of insulator defects in complex outdoor environments. This research contributes to advancing the field of power grid infrastructure monitoring and maintenance, ultimately facilitating more effective strategies for mitigating the consequence of insulator defects on power grid system performance and reliability.
Inspired by the ambitions envisioned in the Fourth Industrial Revolution for aquaculture, also known as Aquaculture 4.0, the aquaculture (marine animal farming) industry is seeking to adopt ...data-driven Artificial Intelligence (AI) to help significantly improve business operations. One of the major barriers is the manual annotation of animal behaviour data, which is a time-consuming task that demands high levels of concentration from biologists. To address this challenge, this paper proposes novel automatic animal behaviour monitoring tailored for industrial scenarios. Our approach introduces a real-time machine-learning-based instance segmentation system that is specialised for underwater environments, where large groups of shrimp are farmed. The implemented system achieves an accuracy rate of 89% at 30 frames per second (fps) and can accurately detect shrimp in high-density areas under poor lighting conditions and high turbidity waters, despite the challenges of occlusion and overlapping. A key innovation of our method is the implementation of a new density cluster algorithm for time series and video analysis. This approach provides a more efficient and accurate way of monitoring animal behaviour, significantly saving time and effort for biologists and advancing the capabilities of automated aquaculture systems.
•Novel AI-system to enhance animal detection accuracy in high-density areas.•Enhanced DBScan algorithm for time series for density-based spatial clustering.•AI model with robustness in handling occlusion, turbidity, and overlapping.
•A lightweight architecture for dead tree detection is designed called LDS-YOLO.•A novel feature extraction network for small target detection is proposed.•The detection accuracy is improved by dense ...connectivity and SPP with SoftPool.•The state-of-the-art models for dead tree detection are compared.
The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest.
Object detection techniques are the foundation for the artificial intelligence field. This research paper gives a brief overview of the You Only Look Once (YOLO) algorithm and its subsequent advanced ...versions. Through the analysis, we reach many remarks and insightful results. The results show the differences and similarities among the YOLO versions and between YOLO and Convolutional Neural Networks (CNNs). The central insight is the YOLO algorithm improvement is still ongoing.This article briefly describes the development process of the YOLO algorithm, summarizes the methods of target recognition and feature selection, and provides literature support for the targeted picture news and feature extraction in the financial and other fields. Besides, this paper contributes a lot to YOLO and other object detection literature.
Cotton detection is the localization and identification of the cotton in an image. It has a wide application in robot harvesting. Various modern algorithms use deep learning techniques for detection ...of fruits/flowers. As per the survey, the topics travelled include numerous algorithms used, and accuracy obtained on using those algorithms on their data set. The limitations and the advantages in each paper, are also discussed. This paper focuses on various fruit detection algorithms- the Faster RCNN, the RCNN, YOLO. Ultimately, a rigorous survey of many papers related to the detection of objects like fruits/flowers, analysis of the assets and faintness of each paper leads us to understanding the techniques and purpose of algorithms.
In recent years, face detection algorithms based on deep learning have made great progress. Nevertheless, the effective utilization of face detectors for small and occlusion faces remains ...challenging, primarily stemming from the limitations in pixel information and the presence of missing features. In this paper, we propose a novel real-time face detector, YOLO-FaceV2, built upon the YOLOv5 architecture. Our approach introduces a Receptive Field Enhancement (RFE) module designed to extract multi-scale pixel information and augment the receptive field for accurately detecting small faces. To address issues related to face occlusion, we introduce an attention mechanism termed the Separated and Enhancement Attention Module (SEAM), which effectively focuses on the regions affected by occlusion. Furthermore, we propose a Slide Weight Function (SWF) to mitigate the imbalance between easy and hard samples. The experiments demonstrate that our YOLO-FaceV2 achieves performance exceeding the state-of-the-art on the WiderFace validation dataset. Source code and pre-trained model are available at https://github.com/Krasjet-Yu/YOLO-FaceV2.
•Proposed an YOLO-FaceV2 detector to address face detection.•Good performance under face occlusion and varying scales.•Designed a novel weighting function alleviated the problem of imbalanced samples.•Detection results on the WiderFace validation dataset are 98.6%, 97.9% and 91.9%.•Achieved state-of-the-art performance on the easy and medium subset of WiderFace dataset.
Potholes are considered the main factor for road defects, which leads to road status deterioration, which, consequently will lead to increased road accidents. The first step in road maintenance is to ...inspect the road surface and then accurately detect potholes. However, manually identifying them is costly and time-consuming. In this study, unmanned aerial vehicle (UAV) imagery was used to create orthophotos of the roads and, using deep learning methods, potholes were detected. The used deep learning method in this study is the "you only look once" (YOLO) algorithm. YOLO is one of the "deep learning-based approaches" to detecting objects and is a single-stage network which requires only one forward propagation across the neural network and focuses on the entire image. The fourth version of YOLO is YOLOV4, which has two different architectures (YOLOv4 and YOLOv4-tiny). Two roads were chosen as the study areas, and to generate the orthophotos of the roads, UAV was used to acquire images. To train both methods in the process of detecting potholes using deep learning, 5300 images were used, 90% used for training and 10% applied for testing. The two used architectures were trained for 6000 iterations. Both methods were evaluated based on the average loss, mean average precision (mAP), and training and testing time. The results showed that the (mAP) values for YOLOv4 and YOLOv4-tiny were 91. 2% and 85.7%, respectively. At the end of the 6000 iterations, the average loss for YOLOv4 is 0.30% and for YOLOv4-tiny is 0.34%. In the training process, YOLOv4 needs 29 seconds for each iteration, while YOLOv4-tiny requires only 8 seconds. In the test process, YOLOv4-tiny is faster at detecting potholes than YOLOv4. The approaches were tested on orthophotos created by processing UAV photos. When comparing the detection of both architectures with visual detection, the results showed that YOLOv4 was able to detect most of the potholes on roads, but YOLOv4-tiny detected a lower number of potholes.
Hardware-Accelerated YOLOv5 Based on MPSoC Liu, Junjiang; Mao, Mingyu; Gao, Jianlong ...
Journal of physics. Conference series,
03/2024, Volume:
2732, Issue:
1
Journal Article
Peer reviewed
Open access
Abstract This paper details the development of a hardware acceleration system for YOLOv5, focusing on flame detection as its primary application. The implementation leverages the APU and DPU ...functionalities integrated into the Zynq UltraScale+ MPSoC XCZU7EV core. The proposed solution addresses the challenge of achieving real-time target detection on mobile terminals, ensuring both real-time operation and ultralow power consumption of YOLOv5. Notably, our design approach facilitates the deployment of all target detection algorithms under TensorFlow for mobile devices. To optimize model efficiency, we employ saturated linear mapping quantization with calibration. This technique maps model weights, double bases, and activations from 32-bit to 8-bit, incurring only a 1.64% accuracy loss. The data flow design is realized through efficient data exchange between DDR, APU, and DPU, utilizing the AXI4 bus architecture. Image pre-processing and post-processing tasks are executed on the APU, while neural network inference occurs on the DPU. Our accelerated system demonstrates compelling experimental results: maintaining a detection speed of 56FPS, achieving an accuracy of 36.56% on the COCO2014 dataset, and exhibiting a total system power consumption of only 4.147W. Furthermore, the energy consumption ratio is measured at 15.41GOPS/W, surpassing the RTX A6000 graphics card by a factor of 55.
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