Vehicle and wheel detection: a novel SSD-based approach and associated large-scale benchmark dataset

Fu, Jiayue; Zhao, Cairong; Xia, Ye; Liu, Wenbin

doi:10.1007/s11042-019-08523-y

Cited by 9 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning has come to the fore in recent years as an artificial intelligence approach that provides successful results in many image processing applications from image enhancement (such as [ 52 ]) to object identification (such as [ 53 , 54 ]).…”

Section: Methodsmentioning

confidence: 99%

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Yaşar

Ceylan

2020

Appl Intell

View full text Add to dashboard Cite

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

show abstract

Section: Methodsmentioning

confidence: 99%

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Yaşar

Ceylan

2020

Appl Intell

View full text Add to dashboard Cite

show abstract

“…9. Our technique eradicates the problem of tiny vehicle to great extent which is the huge challenge in MSSD300 * [12]. Our model is single-pass, Fast and accurate and does not affect the output image dimension as offset heatmap maps the predicted coordinates to original through local offset, accumulated at early stage.…”

Section: Discussionmentioning

confidence: 99%

“…We used single step model CenterNet [15] with different backbones HourGlass [16], ResNet-50, ResNet-101 [17] architectures for vehicle detection. To make best comparison between speed and accuracy we evaluate our hypothesis on SOTA one-step model MSSD300 * [12] (InceptionV2s, mobileNet, ResNet101) and SMOKE [14] with varying backbones because these model outperforms in object detection. To optimize the model we use Adam optimizer.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

Ayesha¹,

Iqbal²,

Ahmad³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System (ITS) and reduces road accidents. The major obstacles in automatic detection of tiny vehicles are due to occlusion, environmental conditions, illumination, view angles and variation in size of objects. This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area. In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks (DNN) namely CenterNet. Substantially DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded area. Primarily there are two categories of deep learning models single-step and two-step. A single forward pass model is the one in which detection is performed directly to possible location over dense sampling, wherein two-step models incorporated by Region proposals followed by object detection. We in this research scrutinize one-step State of the art (SOTA) model CenteNet as proposed recently with three different feature extractor ResNet-50, HourGlass-104 and ResNet-101 one by one. We train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300 * which depicts performance improvement by 20.2% mAP and SMOKE by with 13.2% mAP respectively. Effectiveness of CenterNet can be justified through the huge improved performance. The performance of our model is evaluated on KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) benchmark dataset with different backbones such as ResNet-50 gives 62.3% mAP ResNet-101 82.5% mAP, last but not the least HourGlass-104 outperforms with 98.2% mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature extractors. We also compare our model with other SOTA techniques.

show abstract

“…With time, tiny SSD has been introduced to provide reliable performance compared to tiny Yolo on the VOC07 dataset [ 190 ]. The authors can use SSD to optimize the algorithm to detect objects such as vehicles and wheels using optimized SSD [ 41 ]. Deconvolutional Single Shot Detector (DSSD) has an extended version of faster RCNN in, which ResNet-101(Backbone) adopted.…”

Section: Techniques For Object Detectionmentioning

confidence: 99%

Tools, techniques, datasets and application areas for object detection in an image: a review

Kaur

Singh

2022

Multimed Tools Appl

View full text Add to dashboard Cite

Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.

show abstract

Vehicle and wheel detection: a novel SSD-based approach and associated large-scale benchmark dataset

Cited by 9 publications

References 9 publications

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

Tools, techniques, datasets and application areas for object detection in an image: a review

Contact Info

Product

Resources

About