UAV High-Voltage Power Transmission Line Autonomous Correction Inspection System Based on Object Detection

Li, Ziran; Wang, Qi; Zhang, Tianyi; Ju, Cheng; Suzuki, Satoshi; Namiki, Akio

doi:10.1109/jsen.2023.3260360

Cited by 15 publications

(7 citation statements)

References 33 publications

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“…Zhang et al 15 reconstructed the backbone network of YOLOv4 using the lightweight GhostNet 16 integrated with the (C-SE) attention mechanism, reducing computational complexity and decreasing background interference. Li et al 17 replaced the backbone network of YOLOX with the feature-extraction-capable lightweight network MobileNetV3. 18 They substituted the traditional convolution in the neck with GhostNet and depthwise convolution (DWConv) and introduced the channel attention (CA) 19 mechanism at the neck entrance to enhance the model's object localization capability.…”

Section: Introductionmentioning

confidence: 99%

LRSNet: a high-efficiency lightweight model for object detection in remote sensing

Zhu,

Miao,

Wang

2024

J. Appl. Rem. Sens.

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Unmanned aerial vehicles (UAVs) exhibit the ability to flexibly conduct aerial remotesensing imaging. By employing deep learning object-detection algorithms, they efficiently perceive objects, finding widespread application in various practical engineering tasks. Consequently, UAV-based remote sensing object detection technology holds considerable research value. However, the background of UAV remote sensing images is often complex, with varying shooting angles and heights leading to difficulties in unifying target scales and features. Moreover, there is the challenge of numerous densely distributed small targets. In addition, UAVs face significant limitations in terms of hardware resources. Against this background, we propose a lightweight remote sensing object detection network (LRSNet) model based on YOLOv5s. In the backbone of LRSNet, the lightweight network MobileNetV3 is used to substantially reduce the model's computational complexity and parameter count. In the model's neck, a multiscale feature pyramid network named CM-FPN is introduced to enhance the detection capability of small objects. CM-FPN comprises two key components: C3EGhost, based on GhostNet and efficient channel attention modules, and the multiscale feature fusion channel attention mechanism (MFFC). C3EGhost, serving as CM-FPN's primary feature extraction module, possesses lower computational complexity and fewer parameters, as well as effectively reducing background interference. MFFC, as the feature fusion node of CM-FPN, can adaptively weight the fusion of shallow and deep features, acquiring more effective details and semantic information for object detection. LRSNet, evaluated on the NWPU VHR-10, DOTA V1.0, and VisDrone-2019 datasets, achieved mean average precision of 94.0%, 71.9%, and 35.6%, with Giga floating-point operations per second and Param (M) measuring only 5.8 and 4.1, respectively. This outcome affirms the efficiency of LRSNet in UAV-based remote-sensing object detection tasks.

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Section: Introductionmentioning

confidence: 99%

LRSNet: a high-efficiency lightweight model for object detection in remote sensing

Zhu,

Miao,

Wang

2024

J. Appl. Rem. Sens.

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“…The vast amount of image data collected by traditional drones and inspection robots not only poses significant challenges to conventional manual verification methods but also markedly reduces monitoring efficiency. Moreover, these images are often situated in complex background environments, making the detection of targets challenging [2]. Therefore, using suitable detection methods to monitor the construction machinery and equipment around the line, prevent the intrusion of large construction machinery into the transmission line field, and quickly discover the existing risks can help transmission line fault diagnosis and problem early warning.…”

Section: Introductionmentioning

confidence: 99%

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

Ji,

Zhang,

Huang

et al. 2023

IET Generation Trans & Dist

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The distribution of transmission lines is complex, influenced by terrain and topography, making their maintenance and management challenging. With the advancement of global mechanization, various types of construction machinery and large‐scale mechanical equipment are continually increasing. Accidental contact of construction machinery near transmission lines poses a serious threat to the stability of the power system. Traditional drones and inspection robots face difficulties in achieving real‐time monitoring of construction machinery and equipment around transmission lines, quickly identifying potential risks. Conventional image processing techniques and convolutional neural networks struggle with effective handling of small targets and densely packed detection tasks involving multiple targets. To address these challenges, this paper proposes an intelligent detection algorithm Swin Transformer attention efficient algorithm‐you only look once (YOLO) (STAE‐YOLO). It is based on the Swin Transformer global self‐attention mechanism, cross‐channel fusion attention mechanism, enhanced small object detection framework, and a focused and efficient regression loss function. The experimental results show that the STAE‐YOLO algorithm model improved by 6.3% in mean average precision, 3.7% in precision, and 3.1% in recall compared to the baseline model. Meanwhile, deploying the window multi‐head self‐attention global self‐attention mechanism in the model can strengthen the global multi‐scale semantic information in detection.

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“…Subsequently, the well-trained policy is directly applied to real-world drones for practical applications. 3.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the well-trained policy is directly applied to real-world drones for practical applications. 3. From a practical perspective, we designed a hybrid reward function to facilitate policy exploration and self-improvement.…”

Section: Introductionmentioning

confidence: 99%

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Model-Free Guidance Method for Drones in Complex Environments Using Direct Policy Exploration and Optimization

Liu

Suzuki

2023

Drones

Self Cite

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In the past few decades, drones have become lighter, with longer hang times, and exhibit more agile performance. To maximize their capabilities during flights in complex environments, researchers have proposed various model-based perception, planning, and control methods aimed at decomposing the problem into modules and collaboratively accomplishing the task in a sequential manner. However, in practical environments, it is extremely difficult to model both the drones and their environments, with very few existing model-based methods. In this study, we propose a novel model-free reinforcement-learning-based method that can learn the optimal planning and control policy from experienced flight data. During the training phase, the policy considers the complete state of the drones and environmental information as inputs. It then self-optimizes based on a predefined reward function. In practical implementations, the policy takes inputs from onboard and external sensors and outputs optimal control commands to low-level velocity controllers in an end-to-end manner. By capitalizing on this property, the planning and control policy can be improved without the need for an accurate system model and can drive drones to traverse complex environments at high speeds. The policy was trained and tested in a simulator, as well as in real-world flight experiments, demonstrating its practical applicability. The results show that this model-free method can learn to fly effectively and that it holds great potential to handle different tasks and environments.

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UAV High-Voltage Power Transmission Line Autonomous Correction Inspection System Based on Object Detection

Cited by 15 publications

References 33 publications

LRSNet: a high-efficiency lightweight model for object detection in remote sensing

LRSNet: a high-efficiency lightweight model for object detection in remote sensing

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

Model-Free Guidance Method for Drones in Complex Environments Using Direct Policy Exploration and Optimization

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