YOLO Algorithm-Based Surrounding Object Identification on Autonomous Electric Vehicle

Fanthony, Irvine Valiant; Husin, Zaenal; Hikmarika, Hera; Dwijayanti, Suci; Suprapto, Bhakti Yudho

doi:10.23919/eecsi53397.2021.9624275

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, although the YOLOv5 [39] model was used for safety vehicle detection mechanisms [40], reservations exist, as it was not developed by the original author of YOLO and is less innovative than previous versions [41]. Tiny architectures for the YOLO algorithm series were proposed in order to use fewer computational resources than the full-scale YOLO series, allowing for higher-speed performance, even on mobile devices or embedded systems [42].…”

Section: Introductionmentioning

confidence: 99%

Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle

Shabestari,

Hosseininaveh,

Remondino

2023

Remote Sensing

View full text Add to dashboard Cite

Motorcycle detection and collision warning are essential features in advanced driver assistance systems (ADAS) to ensure road safety, especially in emergency situations. However, detecting motorcycles from videos captured from a car is challenging due to the varying shapes and appearances of motorcycles. In this paper, we propose an integrated and innovative remote sensing and artificial intelligence (AI) methodology for motorcycle detection and distance estimation based on visual data from a single camera installed in the back of a vehicle. Firstly, MD-TinyYOLOv4 is used for detecting motorcycles, refining the neural network through SPP (spatial pyramid pooling) feature extraction, Mish activation function, data augmentation techniques, and optimized anchor boxes for training. The proposed algorithm outperforms eight existing YOLO versions, achieving a precision of 81% at a speed of 240 fps. Secondly, a refined disparity map of each motorcycle’s bounding box is estimated by training a Monodepth2 with a bilateral filter for distance estimation. The proposed fusion model (motorcycle’s detection and distance from vehicle) is evaluated with depth stereo camera measurements, and the results show that 89% of warning scenes are correctly detected, with an alarm notification time of 0.022 s for each image. Outcomes indicate that the proposed integrated methodology provides an effective solution for ADAS, with promising results for real-world applications, and can be suitable for running on mobility services or embedded computing boards instead of the super expensive and powerful systems used in some high-tech unmanned vehicles.

show abstract

Section: Introductionmentioning

confidence: 99%

Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle

Shabestari,

Hosseininaveh,

Remondino

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Xiang Zhang proposed another new moving target detection method based on the prior knowledge of the airport apron which can make the classification biased towards the foreground, so as to make up for the detection defect [20]. Irvine Valiant Fanthony used the YOLO network to help auto driving vehicles detect targets in real time with high accuracy and good results [21]. Obviously, the network cascade and migration played a major role on promoting the detection ability of UAV images.…”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm for Moving Object Detection in YOLO UAV Videos

Hu¹,

Hu²,

Wang³

et al. 2022

Journal of Compurters

View full text Add to dashboard Cite

<p>Recently, moving object detection (MOD) in UAV (Unmanned Aerial. Vehicle) videos has been widely used in many fields. However, different objects and different algorithms often result in different detection accuracy. SSD (Single Shot MultiBox Detector) series and YOLO (You Only Look Once) version 5 are two popular object detection model, and their performance are always evaluated and compared with other improved method for optimizing detection accuracy. In this paper, an improved YOLO_v5 detection algorithm was proposed to further improve the detection accuracy. It adopted a cascaded inter-frame verification mechanism which is based on the neural network and uses spatial information and integrates object speed and direction as well to improve the detection accuracy of moving objects. To evaluate its performance, the open UAV video data from Stanford University was used to test the algorithms, and three types of moving objects were analyzed. The experimental results demonstrate that the proposed MOD method can improve the detection accuracy of small moving objects, which have a good application value, and can lay a foundation for subsequent related studies.</p> <p> </p>

show abstract