Fast Multi Object Detection and Counting by YOLO V3

Ahmadi, Mohammad H.; Xu, Zichun; Wang, Xinli; Wang, Lei; Shao, Meng; Yu, Youliang

doi:10.1109/cac53003.2021.9727949

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The weights used are the weights from the YOLO V2 and YOLO V3 COCO (Common Objects in Context) datasets. The choice of YOLO as the object detection algorithm is because the mAP (mean average prediction) value of this algorithm outperforms several other methods such as SSD and Faster R-CNN [25]. The workings of the YOLO v3 Tiny network architecture used can be seen in Table 4.…”

Section: Convolution Neural Network and Training Resultsmentioning

confidence: 99%

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

Sudianto

Muslim

Rusli

2023

KINETIK

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Every mobile robot mission starts with the robot being moved to the task site. From there, the robot executes its tasks. A control system is required to move the mobile robot's actuator (which may be in the shape of wheels or legs) and comprehend the environment around the robot to perform these movements (perception). This research aims to develop a technique to control a robot’s movement while detecting obstacles and distances toward an object. The robot is equipped with LIDAR and a camera to perform these tasks. The control is divided into two major parts, low-level and high-level controller. As part of a low-level controller robot, the Model Predictive Control (MPC) method is proposed to help with the control of the wheel while the Artificial Neural Network (ANN) approach to use in this study to identify obstacles and the Convolutional Neural Network (CNN) method for detecting objects, both ANN and CNN as a control for high-level part of the robot. The results of this study can prove that CNN can help detect existing objects with a value of 45% for detecting some objects. The obtained result from the MPC method, which has been combined with an ANN as an obstacle detector, is that the smaller the horizon value, the shorter the time needed to reach the desired coordinates with the result being 45 seconds.

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Section: Convolution Neural Network and Training Resultsmentioning

confidence: 99%

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

Sudianto

Muslim

Rusli

2023

KINETIK

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“… YOLOv3 structure from Ayoosh Kathurias blog post in Towards Data Science [ 67 ]. Reprinted with permission from [ 67 ]. 2018, Ayoosh Kathuria.…”

Section: Methodsmentioning

confidence: 99%

Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks

Gleichauf

Hennemann

Fahlbusch

et al. 2023

Sensors

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The monitoring of vital signs and increasing patient comfort are cornerstones of modern neonatal intensive care. Commonly used monitoring methods are based on skin contact which can cause irritations and discomfort in preterm neonates. Therefore, non-contact approaches are the subject of current research aiming to resolve this dichotomy. Robust neonatal face detection is essential for the reliable detection of heart rate, respiratory rate and body temperature. While solutions for adult face detection are established, the unique neonatal proportions require a tailored approach. Additionally, sufficient open-source data of neonates on the NICU is lacking. We set out to train neural networks with the thermal-RGB-fusion data of neonates. We propose a novel indirect fusion approach including the sensor fusion of a thermal and RGB camera based on a 3D time-of-flight (ToF) camera. Unlike other approaches, this method is tailored for close distances encountered in neonatal incubators. Two neural networks were used with the fusion data and compared to RGB and thermal networks. For the class “head” we reached average precision values of 0.9958 (RetinaNet) and 0.9455 (YOLOv3) for the fusion data. Compared with the literature, similar precision was achieved, but we are the first to train a neural network with fusion data of neonates. The advantage of this approach is in calculating the detection area directly from the fusion image for the RGB and thermal modality. This increases data efficiency by 66%. Our results will facilitate the future development of non-contact monitoring to further improve the standard of care for preterm neonates.

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Wi-Fi Controlled Smart Robot for Objects Tracking and Counting

Prithviraj,

Sriharipriya

2023

Lecture Notes in Electrical Engineering

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Fast Multi Object Detection and Counting by YOLO V3

Cited by 6 publications

References 13 publications

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks

Wi-Fi Controlled Smart Robot for Objects Tracking and Counting

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