Pedestrian Detection Based on YOLO Network Model

Lan, Wenbo; Dang, Jianwu; Wang, Yangping; Wang, Song

doi:10.1109/icma.2018.8484698

Cited by 193 publications

(78 citation statements)

References 3 publications

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“…The reported results showed better accuracy as compared to the original tiny-yolov3 with 73.98% precision rate. An improved YOLO structured detection algorithm was presented by Lan et al [3]. The modified model embedded a new Route and Reorg layers which help in learning shallow pedestrian feature information.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…One promising solution to address such a challenge is by employing deep learning models. Recently, various deep learning-based approaches have been implemented such as Faster R-CNN [2], YOLO [3], and tiny-YOLO [4]. Motivated by the cheap cost of the drone and the success of these deep learning models in handling image-based object detection problems [2], this work is aiming to adopt Faster R-CNN to handle the problem of pedestrian detection from drone-based images.…”

Section: Introductionmentioning

confidence: 99%

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Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

et al. 2020

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Pedestrian detection from a drone-based images has many potential applications such as searching for missing persons, surveillance of illegal immigrants, and monitoring of critical infrastructure. However, it is considered as a very challenge computer vision problem due to the variations in camera point of view, distance from pedestrian, changes in illuminations and weather conditions, variation in the surrounding objects, as well as present of human-like objects. Recently, deep learningbased models are getting more attention, and they have proven a great success in many object detection problems such as the detection of faces, breast masses, and vehicles. As such, this work aims to develop a deep learning-based model that will be applied for the problem of pedestrian detection from a drone-based images. Particularly, faster region-based convolutional neural network (Faster R-CNN) will be used to search for the present of a pedestrian inside the captured drone-based images. To assess the performances, a total of 1500 images were collected by S30W drone and these images were captured at different places, with various views and weather conditions, and at daytime and night-time. Results show that Faster R-CNN was able to achieve a promising result with 98% precision, 99% recall, and 98% F1 measure. Further analysis has been conducted by comparing the outcomes of Faster R-CNN with YOLO deep model on UAV123 publicly available dataset. The reported results indicated that both detection models almost reported very similar results.

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Section: Deep Learning Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

et al. 2020

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“…The YOLO neural network is based on the regression method to complete the target detection instead of the regional recommendation. It was proposed by Ross et al in 2015 [20], which mainly transforms the multi-classification problem into a regression problem to solve the image detection. The classification and localization problems are solved by the same regression algorithm, which greatly improves the detection speed and achieves real-time effects in the field of general image target detection.…”

Section: Yolomentioning

confidence: 99%

Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

Zhou

Wu³

et al. 2019

Electronics

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The traditional oil well monitoring method relies on manual acquisition and various high-precision sensors. Using the indicator diagram to judge the working condition of the well is not only difficult to establish but also consumes huge manpower and financial resources. This paper proposes the use of computer vision in the detection of working conditions in oil extraction. Combined with the advantages of an unmanned aerial vehicle (UAV), UAV aerial photography images are used to realize real-time detection of on-site working conditions by real-time tracking of the working status of the head working and other related parts of the pumping unit. Considering the real-time performance of working condition detection, this paper proposes a framework that combines You only look once version 3 (YOLOv3) and a sort algorithm to complete multi-target tracking in the form of tracking by detection. The quality of the target detection in the framework is the key factor affecting the tracking effect. The experimental results show that a good detector makes the tracking speed achieve the real-time effect and provides help for the real-time detection of the working condition, which has a strong practical application.

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“…In [11] is presented a pedestrian detection based on a variation of YOLO network model, (three layers were added to the original one) in order to join the shallow layer pedestrian features to the deep layer pedestrian features and connect the high, and low-resolution pedestrian features.…”

Section: Related Workmentioning

confidence: 99%

Detection of Pedestrian Actions Based on Deep Learning Approach

Pop¹

2019

Studia UBB Informatica

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The pedestrian detection has attracted considerable attention from research due to its vast applicability in the field of autonomous vehicles. In the last decade, various investigations were made to find an optimal solution to detect the pedestrians, but less of them were focused on detecting and recognition the pedestrian's action. In this paper, we converge on both issues: pedestrian detection and pedestrian action recognize at the current detection time (T=0) based on the JAAD dataset, employing deep learning approaches. We propose a pedestrian detection component based on Faster R-CNN able to detect the pedestrian and also recognize if the pedestrian is crossing the street in the detecting time. The method is in contrast with the commonly pedestrian detection systems, which only discriminate between pedestrians and non-pedestrians among other road users.

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Pedestrian Detection Based on YOLO Network Model

Cited by 193 publications

References 3 publications

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

Detection of Pedestrian Actions Based on Deep Learning Approach

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