A modified deep learning architecture for vehicle detection in traffic monitoring system

Haritha, H.; Kumar, T. Senthil

doi:10.1080/1206212x.2019.1662171

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…We use the KITTI dataset to train SSD [37], RFCN [38], Faster R-CNN [20], YOLOv3 [24], FE-CNN [25], RIAC [26], MVD [39] and our method respectively. The accuracy and detection time are then evaluated on the testing set, as shown in Table 3.…”

Section: Comparison With Sota Methodsmentioning

confidence: 99%

“…Among them, S and T represent the size and center of the rectangular frame of the object, respectively, and R IOU (S, T) represents the overlap ratio of the two rectangular frames. After weighing the average R IOU and the number of anchor boxes, as shown in Figure 7, take 15 anchor boxes, which are (10, 95), (13,36), (19,156), (22,51), (29,80), (34,255), (39,56) As shown in Figure 7, ideally, the larger the number of anchor boxes, the better. However, the number of anchor boxes affects the execution efficiency of the algorithm.…”

Section: Multi-scale Anchor Box Designmentioning

confidence: 99%

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Part-Aware Refinement Network for Occlusion Vehicle Detection

Wang

Huang

et al. 2022

Electronics

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Traditional machine learning approaches are susceptible to factors such as object scale, occlusion, leading to low detection efficiency and poor versatility in vehicle detection applications. To tackle this issue, we propose a part-aware refinement network, which combines multi-scale training and component confidence generation strategies in vehicle detection. Specifically, we divide the original single-valued prediction confidence and adopt the confidence of the visible part of the vehicle to correct the absolute detection confidence of the vehicle. That reduces the impact of occlusion on the detection effect. Simultaneously, we relabel the KITTI data, adding the detailed occlusion information of the vehicles. Then, the deep neural network model is trained and tested using the new images. Our proposed method can automatically extract the vehicle features and solve larger error problems when locating vehicles in traditional approaches. Extensive experimental results on KITTI datasets show that our method significantly outperforms the state-of-the-arts while maintaining the detection time.

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Section: Comparison With Sota Methodsmentioning

confidence: 99%

Section: Multi-scale Anchor Box Designmentioning

confidence: 99%

Part-Aware Refinement Network for Occlusion Vehicle Detection

Wang

Huang

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…Recently, Fractionally Autoregressive Integrated Moving Average (FARIMA) has been developed for measuring the soil temperature in [21]. The obtained results are compared with the results from GEP and Artificial Intelligence model and found that the FARIMA model is inadequate in determining for the extreme rates of soil temperature [22,23].…”

Section: Related Workmentioning

confidence: 99%

Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

Sumathi¹,

Karthikeyan²,

Kavitha³

et al. 2023

Computer Systems Science and Engineering

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Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around. Hence, the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield. In present decade, the application of deep learning models in many fields of research has created greater impact. The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model, leads to the incorporation of deep learning method to predict the soil quality. With that concern, this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning (ISQP-DL). The work considers the chemical, physical and biological factors of soil in particular area to estimate the soil quality. Firstly, pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression (DNNR) model. Secondly, soil nutrient data has been given as second input to the DNNR model. By utilizing this data set, the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated. For training and testing, the model uses Deep Neural Network Regression (DNNR), by utilizing the dataset. The results show that the proposed model is effective for SQP (Soil Quality Prediction Model) with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy. The results show that the proposed model achieves 96.7% of accuracy rate compared with existing models.

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“…Deep-learning (DL) algorithms helps in designing models that can be used in many fields. DL has been utilized to make models that can recognize Covid from Chest X-Ray images [9][10][11]. A DL model proposed by Wong and Wang [12] for COVID19 detection (COVID-Net), gave an accuracy of 92.4%.…”

Section: A Related Workmentioning

confidence: 99%

Detection And Diagnosis Of Covid-19 From Chest X-Ray Images

Kishore

Sidhartha

Reddy

et al. 2021

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)

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The Novel Coronavirus 2019 (COVID-2019) spread quickly around the planet and turned into an undermining pandemic. The early detection of Covid disease is one of the principal challenges and needs on the planet. Early detection helps in controlling the spread of infection. Deep learning has acquired great significance in clinical picture investigation, illustrating improved performance compared to conventional machine learning framework. In this work, a DL based model is proposed for recognizing and characterizing the inconsistencies in chest X-Ray pictures and arranging as unaffected, Covid affected, or Pneumonia. Considering the information inadequacy in clinical space, VGG architecture is utilized as the pre-trained models for building the model for recognition. The quality of the X-Ray pictures and noise present in the pictures influence the decision making leading to highfalse positives and false negatives. In the current model, pre-processed pictures are fed as input to the DL model accomplishing a maximum accuracy of 96.56%. The proposed model outperforms the DL model without pre-processing with a false positive rate of 0.024 and a false negative rate of 0.026.

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A modified deep learning architecture for vehicle detection in traffic monitoring system

Cited by 15 publications

References 18 publications

Part-Aware Refinement Network for Occlusion Vehicle Detection

Part-Aware Refinement Network for Occlusion Vehicle Detection

Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

Detection And Diagnosis Of Covid-19 From Chest X-Ray Images

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