Real-Time Human Detection Using Deep Learning on Embedded Platforms: A Review

Rahmaniar, Wahyu; Hernawan, Ari

doi:10.18196/26123

Cited by 12 publications

(6 citation statements)

References 35 publications

(37 reference statements)

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“…In other words, it is a method of solving classification and localization problems at the same time. YOLO [18], TPH-YOLOv5 [19], SSD [20], SSD MobileNet [21], Focal Loss [22], and RefineDet [23]; are representative algorithm of 1-stage detector. While it was popular in the past, Fast R-CNN has an inefficient problem in learning and execution speed because the candidate area generation module is performed in a separate module independently of CNN [24].…”

Section: Yolov5_ours Networkmentioning

confidence: 99%

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

2022

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With the recent development of drone technology, object detection technology is emerging, and these technologies can also be applied to illegal immigrants, industrial and natural disasters, and missing people and objects. In this paper, we would like to explore ways to increase object detection performance in these situations. Photography was conducted in an environment where it was confusing to detect an object. The experimental data were based on photographs that created various environmental conditions, such as changes in the altitude of the drone, when there was no light, and taking pictures in various conditions. All the data used in the experiment were taken with F11 4K PRO drone and VisDrone dataset. In this study, we propose an improved performance of the original YOLOv5 model. We applied the obtained data to each model: the original YOLOv5 model and the improved YOLOv5_Ours model, to calculate the key indicators. The main indicators are precision, recall, F-1 score, and mAP (0.5), and the YOLOv5_Ours values of mAP (0.5) and function loss were improved by comparing it with the original YOLOv5 model. Finally, the conclusion was drawn based on the data comparing the original YOLOv5 model and the improved YOLOv5_Ours model. As a result of the analysis, we were able to arrive at a conclusion on the best model of object detection under various conditions.

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Section: Yolov5_ours Networkmentioning

confidence: 99%

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

2022

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“…The authors in [43] studied human detection for small-sized UAVs with limited computer power benefiting from a YOLO detection algorithm. In [44], the authors proposed real-time detection on embedded platforms by using deep learning.…”

Section: Related Workmentioning

confidence: 99%

A Novel Fusion Method With Thermal and RGB-D Sensor Data for Human Detection

Ozcan

Çetin

2022

IEEE Access

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Human detection methods are widely used in various fields such as autonomous vehicles, video surveillance, and rescue systems. To provide a more effective detection system, different types of sensor data (i.e. optics, thermal, and depth data) may be used together as hybrid information. Fortifying object detection, based on optical data and additional sensor data, such as depth and thermal data, also represents information regarding the distance and temperature of classified objects that can be used for video surveillance, rescue systems, and various applications. In this study, a simple and effective method is introduced to fuse RGB-D and thermal sensor data to achieve a more accurate form of human detection. To accurately combine the sensors, they are physically fixed to each other, and the relationship between them is determined using a novel method. The feature points on the optical and thermal images are extracted and matched successfully using computer vision. The proposed method is completely brand-free, easy to implement, and can be used in real-time applications. Using both thermal and optical data, humans are classified as benefiting from a widely used object detection method. The performance of the presented method is tested with a newly generated dataset. The proposed method boosts human detection accuracy by 5% when compared to the use of only optical data and by 37% when compared to the use of thermal data with COCO Dataset upon YOLOv4 neural network weights. After training with the newly generated dataset, the detection accuracy increases by 18% compared with the best results of single sensor usage.INDEX TERMS Data fusion; human detection; image processing.

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“…It takes a lot of time to detect faulty devices, which is affected by the screen size and the light intensity. With the development of artificial intelligence technology, the deep learning method has been widely used in object tracking [ 3 , 4 ], image super-resolution reconstruction [ 5 ], image dehazing [ 6 ], and defective transmission device detection [ 7 ]. The images of transmission line devices captured by the unmanned aerial vehicle are transmitted via a wireless mobile communication network to a server with high computing power.…”

Section: Introductionmentioning

confidence: 99%

Fast Detection of Defective Insulator Based on Improved YOLOv5s

Zhao

Mengjun

Cui

et al. 2022

Computational Intelligence and Neuroscience

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Defective insulator detection is an essential part of transmission line inspections based on unmanned aerial vehicles. It can timely discover insulator defects and repair them to avoid a power transmission accident. The detection speed of defective insulators based on artificial intelligence directly affects inspection efficiency. To improve the detection speed of defective insulators based on YOLOv5s, an improved detection method with faster detection speed and acceptable precision is proposed. First, a new ResNet unit with three branches is designed based on depthwise separable convolution with kernel three and average pooling. To reduce parameters, the new ResNet unit is used to replace the original ResNet unit used in the CSP1_X module in YOLOv5s. Besides, we also introduce channel shuffle in the CSP1_X module to facilitate the flow of feature information from different channels. Second, a new residual CBL module is designed based on depthwise separable and standard convolution. The new residual CBL module is used to replace the two CBL modules used in the CSP2_X module in YOLOv5s to reduce parameters and extract more useful features. Third, we design a separate, coordinated attention module by introducing location information into channel attention. The new attention module is added to the end of the CSP2_X module to improve the ability to extract insulator location information. Besides, we also use convolution to replace the focus model to reduce computation. Compared with defective insulator detection methods, the proposed method has smaller parameters, floating-point operations per second, and higher frames per second. Although it has lower mean precision, it has a faster detection speed. Besides, the increase in detection speed is greater than the decrease in mean precision.

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Real-Time Human Detection Using Deep Learning on Embedded Platforms: A Review

Cited by 12 publications

References 35 publications

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

A Novel Fusion Method With Thermal and RGB-D Sensor Data for Human Detection

Fast Detection of Defective Insulator Based on Improved YOLOv5s

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