Tong Gan¹, Shuo Dong¹, Shiyou Wang¹, Jiaxin Li^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 643-660, 2024, DOI:10.32604/cmc.2024.051408

Abstract With the rapid development of urban rail transit, the existing track detection has some problems such as low efficiency and insufficient detection coverage, so an intelligent and automatic track detection method based on UAV is urgently needed to avoid major safety accidents. At the same time, the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices. As a result, the Dispersed Computing (DCOMP) architecture enables collaborative computing between devices in the Internet of Everything (IoE), promotes low-latency and efficient cross-wide applications, and… More >

Liang Zeng^1,2, Mai Hu¹, Chenning Zhang¹, Quan Yuan¹, Shanshan Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1677-1709, 2024, DOI:10.32604/cmc.2024.049717

Abstract Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines. To enhance the performance and alleviate the limitations of the Northern Goshawk Optimization (NGO) algorithm, particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes, this study introduces an advanced Improved Northern Goshawk Optimization (INGO) algorithm. This algorithm incorporates a multifaceted enhancement strategy to boost operational efficiency. Initially, a tent chaotic map is employed in the initialization phase to generate a diverse initial population, providing high-quality feasible solutions. Subsequently, after… More >

Jingyu Wang¹, Ping Jiang^1,*, Jianjun Qi²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1889-1918, 2024, DOI:10.32604/cmes.2024.049813

Abstract The unmanned aerial vehicle (UAV) swarm plays an increasingly important role in the modern battlefield, and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm. Due to the high cost and long duration of operational tests, it is essential to plan the test in advance. To solve the problem of planning UAV swarm operational test, this study considers the multi-stage feature of a UAV swarm mission, composed of launch, flight and combat stages, and proposes a method to find test plans that can maximize mission reliability.… More >

Xiangyan Tang^1,2, Chengchun Ruan^1,2,*, Xiulai Li^2,3, Binbin Li^1,2, Cebin Fu^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 983-1003, 2024, DOI:10.32604/cmc.2024.047541

Abstract Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to… More >

Changfeng Feng¹, Chunping Wang², Dongdong Zhang¹, Renke Kou¹, Qiang Fu^1,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3993-4013, 2024, DOI:10.32604/cmc.2024.048351

Abstract Transformer-based models have facilitated significant advances in object detection. However, their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle (UAV) imagery. Addressing these limitations, we propose a hybrid transformer-based detector, H-DETR, and enhance it for dense small objects, leading to an accurate and efficient model. Firstly, we introduce a hybrid transformer encoder, which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently. Furthermore, we propose two novel strategies to enhance detection performance without incurring additional inference… More >

Asifa Mehmood Qureshi¹, Naif Al Mudawi², Mohammed Alonazi³, Samia Allaoua Chelloug⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3683-3701, 2024, DOI:10.32604/cmc.2024.043611

Abstract Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with… More >

Peng Geng^1,*, Annan Yang², Yan Liu³

Journal on Artificial Intelligence, Vol.6, pp. 43-52, 2024, DOI:10.32604/jai.2024.047642

Abstract This article presents a real-time localization method for Unmanned Aerial Vehicles (UAVs) based on continuous image processing. The proposed method employs the Scale Invariant Feature Transform (SIFT) algorithm to identify key points in multi-scale space and generate descriptor vectors to match identical objects across multiple images. These corresponding points in the image provide pixel positions, which can be combined with transformation equations, allow for the calculation of the UAV’s actual ground position. Additionally, the physical coordinates of matching points in the image can be obtained, corresponding to the UAV’s physical coordinates. The method achieves real-time More >

Chao Chen^1,2, Yankun Chen³, Haohai Jin⁴, Li Chen^5,*, Zhisong Liu³, Haozhe Sun⁴, Junchi Hong⁴, Haonan Wang⁴, Shiyu Fang⁴, Xin Zhang²

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 113-114, 2024, DOI:10.32604/iasc.2024.051760

Abstract This article has no abstract. More >

Ruochen Wang^1,#, Jianjun Dong^2,#, Lishan Jin³, Yuyan Sun³, Taogetao Baoyin², Xiumei Wang^*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 387-411, 2024, DOI:10.32604/phyton.2024.047573

Abstract Grassland biomass is an important parameter of grassland ecosystems. The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge. Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass (AGB) estimation. In order to improve the accuracy of vegetation index inversion of grassland AGB, this study combined ground and Unmanned Aerial Vehicle (UAV) remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis. The narrow band vegetation indices were… More >

John Tshibangu Wa Ilunga^1,*, Donatien Kamutanda Kalombo¹, Olivier Ngoie Inabanza¹, Dikumbwa N’landu^1,2, Joseph Mukalay Muamba^1,3, Patrice Amisi Mwana¹, Urcel Kalenga Tshingomba¹, Junior Muyumba Munganga¹, Catherine Nsiami Mabiala¹

Revue Internationale de Géomatique, Vol.33, pp. 15-35, 2024, DOI:10.32604/rig.2024.046629

Abstract The mutation of spaces observed in the Katangese Copper Belt (KCB) causes significant topographical changes. Some colonial geodetic markers are easily noticeable on many of the hills making up the KCB. These hills are subject to mining which ruins the completeness of the network of triangulations: geometric and trigonometric Katangese. In order to keep control of the latter, the study shows on the one hand the possibility of using SRTM data (Shuttle Radar Topography Mission) in the monitoring of the macro-change of the reliefs, from 442 positions, and on the other hand, an indirect (remote) More >