Computer
Muna Khalaf; Ban N. Dhannoon
Abstract
Semantic segmentation refers to labeling each pixel in the scene to its belonging object. It is a critical task for many computer vision applications that requires scene understanding because It attempts to mimic human perceptual grouping. Despite the unremitting efforts in this field, it is still a ...
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Semantic segmentation refers to labeling each pixel in the scene to its belonging object. It is a critical task for many computer vision applications that requires scene understanding because It attempts to mimic human perceptual grouping. Despite the unremitting efforts in this field, it is still a challenge and preoccupies of researchers. Semantic segmentation performance improved using deep learning rather than traditional methods. Semantic segmentation based on deep learning models requires capturing local and global context information, where deep learning models usually can extract one of them but is challenging to integrate between them. Deep learning based on attention mechanisms can gather between the capturing of local and glopal information, so it is increasingly employed in semantic segmentation. This paper gives an introductory survey of the rising topic attention mechanisms in semantic segmentation. At first, it will discuss the concept of attention and its integration with semantic segmentation requirements. Then, it will review deep learning based on attention mechanisms in semantic segmentation.
shahad ahmed; Saman Hameed Ameen
Abstract
Plant diseases are a severe threat to the environment, economy, and health. Early disease identification remains a challenging task in Iraq due to the scarce of the necessary resources and infrastructure. This paper uses various deep learning algorithms to detect different diseases on plant leaves and ...
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Plant diseases are a severe threat to the environment, economy, and health. Early disease identification remains a challenging task in Iraq due to the scarce of the necessary resources and infrastructure. This paper uses various deep learning algorithms to detect different diseases on plant leaves and detect healthy ones, using an RGB camera as a crucial part of our real-time autonomous greenhouses' robot along with using two datasets, plant-village and cotton dataset, to investigate the best convolutional neural network architecture. The first dataset contained 10,190 images from the plant-village open datasets; it includes four crops with ten distinct classes of diseased and healthy leaves. Moreover, the cotton dataset contained 2,204 images for training and 106 images for testing; it has four classes of diseased and healthy plants and leaves. Different network architectures were tested in this paper for the best suitable lightweight architecture for our mobile robot. Results show that the best performance is 99.908% which achieved by the VGG16 network. The highest accuracy of VGG16 obtained in our research makes it the best tool for our autonomous plant disease detection robot.
Mahmoud M. Mahmoud; Ahmed R. Nasser
Volume 21, Issue 2 , June 2021, , Page 36-43
Abstract
Object detection of autonomous vehicles presents a big challenge forresearchers due to the requirements of accuracy and precision in real-time.This work presents a deep learning approach based on a dual architecturedesign of the network. A highly accurate multi-class network of convolutionalneural networks ...
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Object detection of autonomous vehicles presents a big challenge forresearchers due to the requirements of accuracy and precision in real-time.This work presents a deep learning approach based on a dual architecturedesign of the network. A highly accurate multi-class network of convolutionalneural networks (CNN) is presented for input data classification. A Region-Based Convolutional Neural Networks (Faster R-CNN) network with a modifiedFeature Pyramid Networks (FPN) is used for better detection of tiny objects andYou Only Look Once (YOLOv3) network is used for general detection. Eachnetwork independently detects the existence of an object. The decision maps arethen fused and compared to decide whether an object is present or not. FasterR-CNN with FPN model reported a higher intersection over Union (IoU) andmean average precision (mAP) than the YOLOv3. This approach is reliabledemonstrating an upgrade on the existing state-of-the-art methods of fullyconnected networks.
