Object Detection Using Adaptive Mask RCNN in Optical Remote Sensing Images

Mahmoud, Amira S.; Mohamed, Sayed A.; El-Khoribi, Reda A.; Abdelsalam, Hisham M.

doi:10.22266/ijies2020.0229.07

Cited by 35 publications

(19 citation statements)

References 35 publications

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“…In the figure above, AP parameter indicates the average precision [11,20,26], and AR represents the average recall for both bounding boxes and segmentation. Accordingly, average precision defines how accurate the prediction is.…”

Section: Resultsmentioning

confidence: 99%

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Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

2021

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Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.

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Section: Resultsmentioning

confidence: 99%

“…The developed dataset is manipulated with the inverse gamma correction method to create images representing the different lighting conditions. After this development phase, the Mask R-CNN model [24][25][26][27][28] is trained with this dataset by transfer learning and finetuning techniques [29].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

2021

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“…The proposed workflow was inspired by semantic segmentation due to their booming performance in several applications, such as scene comprehension, 32 processing satellite images, 15,33 and object detection in satellite images. 34 UNet model, 35 which is considered a famous and effective semantic segmentation architecture, is used in the training phase to identify the change regions. In general, UNet employs the traditional encoder-decoder scheme.…”

Section: Proposed Workflowmentioning

confidence: 99%

Hyperspectral change detection based on modification of UNet neural networks

Moustafa

Mohamed

Ahmed

et al. 2021

J. Appl. Rem. Sens.

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The Earth's surface changes continuously due to several natural and humanmade factors. Efficient change detection (CD) is useful in monitoring and managing different situations. The recent rise in launched hyperspectral platforms provides a diversity of spectrum in addition to the spatial resolution required to meet recent civil applications requirements. Traditional multispectral CD algorithms hardly cope with the complex nature of hyperspectral images and their high dimensionality. To overcome these limitations, a CD deep convolutional neural network (CNN) semantic segmentation-based workflow was proposed. The proposed workflow is composed of four main stages, namely preprocessing, training, testing, and evaluation. Initially, preprocessing is performed to overcome hyperspectral image noise and the high dimensionality problem. Random oversampling (ROS), deep learning, and bagging ensemble were incorporated to handle imbalanced dataset. Also, we evaluated the generality and performance of the original UNet model and four variants of UNet, namely residual UNet, residual recurrent UNet, attention UNet, and attention residual recurrent UNet. Three hyperspectral CD datasets were employed in performance assessment for binary and multiclass change cases; all datasets suffer from class imbalance and small region of interest size. Recurrent residual UNet presented the best performance in both accuracy and inference time. Overall, the obtained results imply that deep CNN segmentation models can be utilized to implement efficient CD for hyperspectral imageries. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…As a result, they were able to train the network in a week instead of two months. An adaptive Mask RCNN technique for recognizing multi-class objects in remote sensing images was proposed in this study [34]. To overcome the lack of labeled remote sensing imagery, they used transfer learning, fine-tuning, and augmentation techniques such as rotation, scaling, and illumination conditions.…”

Section: Background Studymentioning

confidence: 99%

Data Augmentation Technique to Expand Road Dataset Using Mask RCNN and Image Inpainting

Saha¹,

Ahmed²,

Ahmed³

et al. 2021

AJCT

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A popular method for training a machine learning model is to use a data-driven approach. This research contributes to expanding the dataset of urban road images without vehicles. Using this method will benefit a variety of existing and new research projects that require an empty road to train their data-driven model. To achieve the desired result, the method combines image segmentation and image inpainting. The model detects the vehicle with Mask RCNN and removes the detected object with image inpainting. Morphological transformation was used to improve the method's efficiency. Using dilation operation of morphological transformation, the mask generated from mask RCNN is enlarged. The results of the experiment support the method's efficacy.

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Object Detection Using Adaptive Mask RCNN in Optical Remote Sensing Images

Cited by 35 publications

References 35 publications

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

Hyperspectral change detection based on modification of UNet neural networks

Data Augmentation Technique to Expand Road Dataset Using Mask RCNN and Image Inpainting

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