An improved tiny-yolov3 pedestrian detection algorithm

Zhang, Yi; Shen, Yongliang; Zhang, Jun

doi:10.1016/j.ijleo.2019.02.038

Cited by 180 publications

(54 citation statements)

References 2 publications

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“…e pyramidal part-based model (PPM) is proposed to obtain a more accurate prediction based on the majority vote of the confidence score of the visible parts by cascading the pyramidal structure. In our Fast DPM [14] Haar + SVM [9] Functional-link net [41] DPM [12] Mixture mask [16] HOG-DWT [25] HOG-LAMM [7] PLL-DPM [20] tiny-yolov3 [49] Figure 11: Performance comparison between the proposed approach and the state-of-the-art approaches. experiments, we trained and tested with different validation subsets on INRIA and PSU datasets.…”

Section: Discussionmentioning

confidence: 99%

“…Another promising YOLO-based architecture also attained improvement on detection of pedestrians. YOLO-based pedestrian detection (Y-PD) [48] and tiny-yolov3 [49] were proposed to modify the network's parameters and structures of the general YOLOv2 detector to identify the suitable characteristic of pedestrians for a better learning. Among these approaches, Y-PD provides the best performance at around 91%, which is 6.5% lower than that of our method.…”

Section: Advances In Multimediamentioning

confidence: 99%

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Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

Thu

Suvonvorn

2020

Advances in Multimedia

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Pedestrian detection and classification are of increased interest in the intelligent transportation system (ITS), and among the challenging issues, we can find limitations of tiny and occluded appearances, large variation of human pose, cluttered background, and complex environment. In fact, a partial occlusion handling is important in the case of detecting pedestrians, in order to avoid accidents between pedestrians and vehicles, since it is difficult to detect when pedestrians are suddenly crossing the road. To solve the partial occlusion problem, a pyramidal part-based model (PPM) is proposed to obtain a more accurate prediction based on the majority vote of the confidence score of the visible parts by cascading the pyramidal structure. The experimental results on the proposed scheme achieved 96.25% accuracy on the INRIA dataset and 81% accuracy on the PSU (Prince of Songkla University) dataset. Our proposed model can be applied in the real-world environment to classify the occluded part of pedestrians with the various information of part representation at each pyramid layer.

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Section: Discussionmentioning

confidence: 99%

Section: Advances In Multimediamentioning

confidence: 99%

Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

Thu

Suvonvorn

2020

Advances in Multimedia

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“…The reported results indicated the superiority of Faster R-CNN as compared with other studied approaches. Further work was proposed by Yi et al [4]. They implemented an improved tiny-yolov3 pedestrian detection algorithm.…”

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%

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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“…To reduce computation, the input layer used color images which was resized to a width of 640 pixels and height of 640 pixels, and the YOLO Layer was used as the output layer. Although YOLOv3 Tiny network only retained 24 convolutional layers, it still retained two YOLO network layers, which greatly reduced the amount of computation and still ensured the accuracy of model recognition [18,19].…”

Section: Dial Gray-scale Transformationmentioning

confidence: 99%

A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

2020

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The pointer instrument has the advantages of being simple, reliable, stable, easy to maintain, having strong anti-interference properties, and so on, which has long occupied the main position of electrical and electric instruments. Though the pointer instrument structure is simple, it is not convenient for real-time reading of measurements. In this paper, a RK3399 microcomputer was used for real-time intelligent reading of a pointer instrument using a camera. Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. Finally, predicted results were uploaded to a server. The system realized automatic identification, numerical reading, an intelligent online reading of pointer data, which has high feasibility and practical value. The experimental results show that the recognition rate of this system was 98.71% and the reading accuracy was 97.42%. What is more, the system can accurately locate the pointer-type instrument area and read corresponding values with simple operating conditions. This achievement meets the demand of real-time readings for analog instruments.

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An improved tiny-yolov3 pedestrian detection algorithm

Cited by 180 publications

References 2 publications

Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

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

A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

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