Target Object Detection from Unmanned Aerial Vehicle (UAV) Images Based on Improved YOLO Algorithm

Arunnehru, J.; Sambandham, Thalapathiraj; Sekar, V.; Ravikumar, Dhanasekar; Loganathan, Vijayaraja; Kannadasan, Raju; Khan, Arfat Ahmad; Wechtaisong, Chitapong; Haq, Mohd Anul; Alhussen, Ahmed; Alzamil, Zamil S.

doi:10.3390/electronics11152343

Cited by 42 publications

(23 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jawaharlalnehru et al proposed target object detection from unmanned aerial vehicle (uav) images based on improved yolo algorithm. This proposed model can be effectively used for real-time target detection for multi-scale targets with reduced misprediction rate due to its superior accuracy [ 20 ]. The YOLO series algorithm uses a multi-scale training and prediction strategy to detect targets of different sizes and shapes.…”

Section: Literature Reviewmentioning

confidence: 99%

Swin-Transformer -YOLOv5 for lightweight hot-rolled steel strips surface defect detection algorithm

Wang,

Dong,

Huang

2024

PLoS ONE

View full text Add to dashboard Cite

An essential industrial application is the examination of surface flaws in hot-rolled steel strips. While automatic visual inspection tools must meet strict real-time performance criteria for inspecting hot-rolled steel strips, their capabilities are constrained by the accuracy and processing speed of the algorithm used to identify defects. To solve the problems of poor detection accuracy, low detection efficiency, and unsuitability of low computing power platforms of the hot-rolled strip surface defect detection algorithm The Swin-Transformer-YOLOv5 model based on the improved one-stage detector is proposed. By employing GhostNet, the model’s lightweight design, and guaranteed detection accuracy are both achieved. The C3 module introduces Swin-Transformer to address the issues of cluttered backdrops of defect photos and easily confused defect categories. With the addition of the CoordAttention module, the model’s capacity to extract defective features is improved, and its performance keeps getting better. The issue of huge differences in different scales and poor detection of small flaws is resolved by employing BiFPN for feature fusion, and the detector’s capacity to adapt to targets of different scales is improved. The experimental results demonstrate that the improved Swin-Transformer-Yolov5 model significantly outperforms the industry-standard target detection algorithms, and the model’s mAP value still improves by 8.39% over the original model while reducing the number of parameters, GFLOPs, and weight by 36.6%, 40.0%, and 34.7%, respectively. The model is better suited for use on low-arithmetic platforms as a result.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Swin-Transformer -YOLOv5 for lightweight hot-rolled steel strips surface defect detection algorithm

Wang,

Dong,

Huang

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…According to the UAV's intended application, we can change the used payload (e.g., RGB or Infrared Radiation (IR) cameras) to be able to perform the mission. One of the most-common UAV applications is surveillance, where we can also automatically track the detected frame objects [73][74][75]. We can also use data fusion techniques to perform Simultaneous Localization And Mapping (SLAM) [76,77], which can also be used for autonomous landing in unknown environments [78].…”

Section: Uav Trackingmentioning

confidence: 99%

Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing

Santos

Lobo

Bernardino

2023

Drones

View full text Add to dashboard Cite

The vast increase in the available computational capability has allowed the application of Particle-Filter (PF)-based approaches for monocular 3D-model-based tracking. These filters depend on the computation of a likelihood function that is usually unavailable and can be approximated using a similarity metric. We can use temporal filtering techniques between filter iterations to achieve better results when dealing with this suboptimal approximation, which is particularly important when dealing with the Unmanned Aerial Vehicle (UAV) model symmetry. The similarity metric evaluation time is another critical concern since we usually want a real-time implementation. We explored, tested, and compared with the same dataset two different types of PFs, (i) an Unscented Bingham Filter (UBiF) and (ii) an Unscented Bingham–Gauss Filter (UBiGaF), using pose optimization in both implementations. Using optimization steps between iterations increases the convergence capability of the filter and decreases the obtained error. A new tree-based similarity metric approach is also explored based on the Distance Transform (DT), allowing a faster evaluation of the possibilities without losing accuracy. The results showed that the obtained pose estimation error is compatible with the automatic landing requirements.

show abstract

“…A contraction is a tool that reduces the length of a distance, and it is at the centre of a wide range of image processing tools. Using general topology and functional analysis, digital topology is linked to the 2D and 3D features of digital images in [18,26].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Digital Image Compression in Some Fixed Point Results on Complete G-metric Space Using Comparison Function

Thirumalai,

Thalapathiraj

2023

Int. J. Anal. Appl.

View full text Add to dashboard Cite

In the present time world, digital images are crucial for various applications, that includes the medical industry, aircraft and satellite imaging, underwater imaging and so on. For this huge quantities of digital images are produced and used by these applications. For a variety of reasons, these images also need to be transmitted and stored. Therefore, a technique known as compression is applied to resolve this storage issue while transmitting these images. In this article, by extending some unique fixed point theorem results for comparison function on a complete symmetric G-metric space are used and it is a new approach. Moreover, this paper focuses on a compression method using the new structure of extended G-contraction mapping as it assists in compressing the size of the image. Thus, grayscale images are compressed using extended G-contraction mapping. And thus, grayscale images can be represented as matrices in this structure (pixel values). Also, similar images of reduced size can be obtained using an appropriate matrix G-metric and extended G-contraction mapping. The size of the matrix can be substantially reduced without losing any quality by controlling the order of sub matrices. These images are easy to store and transmit, with little variation between the original and contracted image.

show abstract

Target Object Detection from Unmanned Aerial Vehicle (UAV) Images Based on Improved YOLO Algorithm

Cited by 42 publications

References 28 publications

Swin-Transformer -YOLOv5 for lightweight hot-rolled steel strips surface defect detection algorithm

Swin-Transformer -YOLOv5 for lightweight hot-rolled steel strips surface defect detection algorithm

Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing

A Novel Approach for Digital Image Compression in Some Fixed Point Results on Complete G-metric Space Using Comparison Function

Contact Info

Product

Resources

About