An Object Detection Framework Based on Deep Features and High-Quality Object Locations

Guan, Yurong; Aamir, Muhammad; Hu, Zhihua; Dayo, Zaheer Ahmed; Rahman, Ziaur; Abro, Waheed Ahmed; Soothar, Permanand

doi:10.18280/ts.380319

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…Following the continuous innovation of deep learning technology, deep learning methods have been widely used in image processing fields such as object detection and image segmentation [ 13 , 14 ]. In much research, it has been shown that an advanced neural network architecture is one of the most challenging and effective ways to enhance image segmentation task performance.…”

Section: Related Workmentioning

confidence: 99%

W-Net: Convolutional neural network for segmenting remote sensing images by dual path semantics

et al. 2023

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In the latest research progress, deep neural networks have been revolutionized by frameworks to extract image features more accurately. In this study, we focus on an attention model that can be useful in deep neural networks and propose a simple but strong feature extraction deep network architecture, W-Net. The architecture of our W-Net network has two mutually independent path structures, and it is designed with the following advantages. (1) There are two independent effective paths in our proposed network structure, and the two paths capture more contextual information from different scales in different ways. (2) The two paths acquire different feature images, and in the upsampling approach, we use bilinear interpolation thus reducing the feature map distortion phenomenon and integrating the different images processed. (3) The feature image processing is at a bottleneck, and a hierarchical attention module is constructed at the bottleneck by reclassifying after the channel attention module and the spatial attention module, resulting in more efficient and accurate processing of feature images. During the experiment, we also tested iSAID, a massively high spatial resolution remote sensing image dataset, with further experimental data comparison to demonstrate the generality of our method for remote sensor image segmentation.

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Section: Related Workmentioning

confidence: 99%

W-Net: Convolutional neural network for segmenting remote sensing images by dual path semantics

et al. 2023

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“…When Guan Yurong and others study target recognition, they divide the target image into a group of initial regions and then group the adjacent regions according to the region features by using clustering and hierarchical strategies, by using the edge detector to extract the edge from the original image, the target detection system can be refined to improve the accuracy of detection [29]. In his other paper, he proposed that by using multi-level segmentation of super-pixel, we can get a high-quality class-independent scheme, extract feature vectors from it, and then map the features with soft-max classifier, to determine the class of each object and use a deep neural network (DNN)-based high-quality target location [30]. Both of these papers subdivide the target image to accurately identify the corresponding target object.…”

Section: Related Workmentioning

confidence: 99%

Research on detecting moving targets with an improved Kalman filter algorithm

2023

KSII TIIS

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As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

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“…Because it has reduced and similar hue values to the model in the background model, a background subtraction result pixel is regarded a probable shadow pixel. We then calculate the number of pixels that can be categorised as shadows after the classification process is complete [31]. The shadow pixels are counted as the exact shadow outline need to be recognized and future actions may be to remove the shadow in a image.…”

Section: Proposed Modelmentioning

confidence: 99%

Cast Shadow Angle Detection in Morphological Aerial Images Using Faster R-CNN

Reddy¹,

Harikiran²

2022

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With the tremendous advancements in digital image processing technology over the last few years, it is now possible to resolve many challenging issues. In light of this, this study proposes that digital image processing can be used to detect shadows in photographs. Since unmanned aerial vehicles and satellite devices have become more common image generating devices. The significant issue in the generated images is of its shadow. Shadows are inevitable in remote sensing photographs, particularly in metropolitan environments, due to the block of high-rise objects and the influence of the sun's altitude. This results in missing information in the shadow zone. The state-of-the-art shadow detection algorithms require manual alignment and predefined specific parameters. Most of those existing algorithms fail to deliver precise results in a variety of lighting and ecological conditions. To overcome these limitations, we propose a framework Multi Layered Linked approach with Tagged Feature Model for Shadow Angle Detection (MLTFM-SAD). The aim of the proposed model is to detect the shadows from aerial photographs and angle of those shadows. The proposed framework is a three-step approach. Initially, the image segmentation is applied on the input images. Second, hybrid Gaussian mixing mode and Otsu's approach is applied on the segmented shadow mask map and corresponding pixel set is generated. As a result, an initial shadow mask map is refined using object spectral attributes and spatial correlations between objects. Finally, the angle at which the shadow appears in the given image is recognised and analysed. The proposed method's performance is compared to that of all current approaches and the results revealed that the proposed model performance levels are superior.

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An Object Detection Framework Based on Deep Features and High-Quality Object Locations

Cited by 9 publications

References 40 publications

W-Net: Convolutional neural network for segmenting remote sensing images by dual path semantics

W-Net: Convolutional neural network for segmenting remote sensing images by dual path semantics

Research on detecting moving targets with an improved Kalman filter algorithm

Cast Shadow Angle Detection in Morphological Aerial Images Using Faster R-CNN

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