Traffic Sign Recognition with Deep Learning: Vegetation Occlusion Detection in Brazilian Environments

Dalborgo, Vanessa Ferreira; Murari, Thiago B.; Madureira, Vinícius Souza; Moraes, Jeremias; Bezerra, Vitor Magno O. S.; Santos, Filipe; Silva, Alexandre; Monteiro, Roberto Luiz Souza

doi:10.3390/s23135919

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…Previous studies addressing the challenge of occlusion for object detection tasks have predominantly come out of interest in self-driving cars, including applications for traffic sign, pedestrian, and car detection [26,27]. There have been a few attempts to quantify the effects on the recall of detection models of vegetation as a source of occlusion.…”

Section: The Effect Of Vegetation and Occlusion On Object Detectionmentioning

confidence: 99%

Modeling the Effect of Vegetation Coverage on Unmanned Aerial Vehicles-Based Object Detection: A Study in the Minefield Environment

Baur,

Dewey,

Steinberg

et al. 2024

Remote Sensing

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An important consideration for UAV-based (unmanned aerial vehicle) object detection in the natural environment is vegetation height and foliar cover, which can visually obscure the items a machine learning model is trained to detect. Hence, the accuracy of aerial detection of objects such as surface landmines and UXO (unexploded ordnance) is highly dependent on the height and density of vegetation in a given area. In this study, we develop a model that estimates the detection accuracy (recall) of a YOLOv8 object’s detection implementation as a function of occlusion due to vegetation coverage. To solve this function, we developed an algorithm to extract vegetation height and coverage of the UAV imagery from a digital surface model generated using structure-from-motion (SfM) photogrammetry. We find the relationship between recall and percent occlusion is well modeled by a sigmoid function using the PFM-1 landmine test case. Applying the sigmoid recall-occlusion relationship in conjunction with our vegetation cover algorithm to solve for percent occlusion, we mapped the uncertainty in detection rate due to vegetation in UAV-based SfM orthomosaics in eight different minefield environments. This methodology and model have significant implications for determining the optimal location and time of year for UAV-based object detection tasks and quantifying the uncertainty of deep learning object detection models in the natural environment.

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Section: The Effect Of Vegetation and Occlusion On Object Detectionmentioning

confidence: 99%

Modeling the Effect of Vegetation Coverage on Unmanned Aerial Vehicles-Based Object Detection: A Study in the Minefield Environment

Baur,

Dewey,

Steinberg

et al. 2024

Remote Sensing

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show abstract

“…In recent years, with the rapid development of deep learning [20][21][22][23], many researchers have applied deep learning to image dehazing and designed a large number of dehazing neural networks. CAI et al [24] proposed the Dehaze-net single-image dehazing network, which was the first to introduce convolutional neural networks into image dehazing tasks.…”

Section: Related Workmentioning

confidence: 99%

Multi-Scale Attention Feature Enhancement Network for Single Image Dehazing

Dong,

Wang,

Sun

et al. 2023

Sensors

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Aiming to solve the problem of color distortion and loss of detail information in most dehazing algorithms, an end-to-end image dehazing network based on multi-scale feature enhancement is proposed. Firstly, the feature extraction enhancement module is used to capture the detailed information of hazy images and expand the receptive field. Secondly, the channel attention mechanism and pixel attention mechanism of the feature fusion enhancement module are used to dynamically adjust the weights of different channels and pixels. Thirdly, the context enhancement module is used to enhance the context semantic information, suppress redundant information, and obtain the haze density image with higher detail. Finally, our method removes haze, preserves image color, and ensures image details. The proposed method achieved a PSNR score of 33.74, SSIM scores of 0.9843 and LPIPS distance of 0.0040 on the SOTS-outdoor dataset. Compared with representative dehazing methods, it demonstrates better dehazing performance and proves the advantages of the proposed method on synthetic hazy images. Combined with dehazing experiments on real hazy images, the results show that our method can effectively improve dehazing performance while preserving more image details and achieving color fidelity.

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Traffic Sign Classification using Deep Learning Comparative Study

Hamza,

Nawal

2024

Procedia Computer Science

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Traffic Sign Recognition with Deep Learning: Vegetation Occlusion Detection in Brazilian Environments

Cited by 8 publications

References 31 publications

Modeling the Effect of Vegetation Coverage on Unmanned Aerial Vehicles-Based Object Detection: A Study in the Minefield Environment

Modeling the Effect of Vegetation Coverage on Unmanned Aerial Vehicles-Based Object Detection: A Study in the Minefield Environment

Multi-Scale Attention Feature Enhancement Network for Single Image Dehazing

Traffic Sign Classification using Deep Learning Comparative Study

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