Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency

Almalki, Faris A.; Albraikan, Amani Abdulrahman; Soufiene, Ben Othman; Ali, Obaid

doi:10.1155/2022/7741535

Cited by 20 publications

(7 citation statements)

References 39 publications

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“…In this work, we used X ∈ R c×h×w to represent the input feature map, c to represent the number of channels of the input feature map, and h and w to express the height and width of the feature map, respectively. The conventional convolution is defined as Equation (3).…”

Section: Ghostconv Modulementioning

confidence: 99%

“…To achieve the "double carbon" goal, renewable energy, represented by photovoltaic power generation, is undoubtedly the main force [2]. As an essential part of the development of the PV industry, the fault detection of PV panels is of great significance in promoting the development of PV energy [3]. With the development of artificial intelligence, the intelligent detection of PV panel faults is becoming a feasible and promising solution.…”

Section: Introductionmentioning

confidence: 99%

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GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

Wang

Zhang

2023

Electronics

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Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections. However, multiple tiny defects on the PV panel surface and the high similarity between different defects make it challenging to accurately identify and detect such defects. This paper proposes an approach named Ghost convolution with BottleneckCSP and a tiny target prediction head incorporating YOLOv5 (GBH-YOLOv5) for PV panel defect detection. To ensure better accuracy on multiscale targets, the BottleneckCSP module is introduced to add a prediction head for tiny target detection to alleviate tiny defect misses, using Ghost convolution to improve the model inference speed and reduce the number of parameters. First, the original image is compressed and cropped to enlarge the defect size physically. Then, the processed images are input into GBH-YOLOv5, and the depth features are extracted through network processing based on Ghost convolution, the application of the BottleneckCSP module, and the prediction head of tiny targets. Finally, the extracted features are classified by a Feature Pyramid Network (FPN) and a Path Aggregation Network (PAN) structure. Meanwhile, we compare our method with state-of-the-art methods to verify the effectiveness of the proposed method. The proposed PV panel surface-defect detection network improves the mAP performance by at least 27.8%.

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Section: Ghostconv Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

Wang

Zhang

2023

Electronics

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“…Construct Z u = {u} NPL u , which is the union of u and NPL u . Then, calculate the average distance between each item i in O k , which is the item set of order k and the elements j of Z u with the minimum distance by (1).…”

Section: Finding a Picking List Similar With New Ordersmentioning

confidence: 99%

“…With the start of the Fourth Industrial Revolution, research and development of robots, AI, and IoT have been studied. Especially, through AI and Unmanned Aerial Vehicle (UAV), robots can perform complex tasks on behalf of humans [1,2]. As the core technologies of autonomous UAV such as mapping, path planning, and localization are studied, UAVs are being used in various fields such as security, logistics, surveillance, and quarantine [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Order Picking Method for Multi-UAV System in Intelligent Warehouse

Han

Jeon

et al. 2022

Remote Sensing

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For the logistics environment, multi-UAV algorithms have been studied for the purpose of order picking in warehouses. However, modern order picking adopts static order picking methods that struggle to cope with increasing volumes of goods because the algorithms receive orders for a certain period of time and pick only those orders. In this paper, by using the modified interventionist method and dynamic path planning, we aim to assign orders received in real-time to multi-UAVs in the warehouse, and to determine the order picking sequence and path of each UAV. The halting and correcting strategy is proposed to assign orders to UAVs in consideration of the similarity between the UAV’s picking list and the orders. A UAV starts picking orders by using the ant colony optimization algorithm for the orders initially assigned. For additional orders, the UAV modifies the picking sequence and UAV’s path in real time by using the k-opt-based algorithm. We evaluated the proposed method by changing the parameters in a simulation of a general warehouse layout. The results show that the proposed method not only reduces completion time compared to the previous algorithm but also reduces UAV’s travel distance and the collapsed time.

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“…Temporary event or short-term large-scale events (e.g., sports, social, cultural) require an extra measure of planning, coordination, and management; thus, they can heavily benefit from CubeSat and its wide range applications such as commercial applications (e.g., telecommunications, remote sensing, highresolution aerial imagery, ship tracking and monitoring). Additionally, such small satellites help in more domains like [7][8][9]:…”

Section: Introductionmentioning

confidence: 99%

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

Almalki¹,

Mutawi²,

Turkistani³

et al. 2022

Adv. sci. technol. eng. syst. j.

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Many consequences can be resulted in mismanagement of crowd, which might get people injured or even lose their lives. Thus, crowd management helps in controlling overcrowded areas during events, and allowing authorities to monitor, manage and reduce incidents. Space science and technology have made huge leap in crowd management, let alone when this technology integrated with Artificial intelligent. Hence, space-based systems like CubeSats are seen as the best approach to help monitoring and providing various reasons. For instance, wide coverage footprint, collect specific area data simultaneously, assist in aerial photography, as well as providing wide range of wireless communications services that can integrate appropriately with AI and wearable devices. This work aims to design a CubeSat vehicle to manage crowds during short-term, large-scale events. The proposed system is, also, coupled with AI framework along with the camera and wearable devices to monitor the crowds' performance continuously by relying on two aspects: Firstly, aerial imaging (e.g., photos and videos). Secondly, using wearable devices that can be worn to monitor vital signs of crowds. Moreover, the proposed system can independently relieve congestion, as well as notify the ground controller of all problems to take further actions. The obtained results confirm that the proposed innovative solution is for crowd management with accuracy reached 95%, and MSE equal to 0.049.

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Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency

Cited by 20 publications

References 39 publications

GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

Dynamic Order Picking Method for Multi-UAV System in Intelligent Warehouse

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

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