Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review

Amjoud, Ayoub Benali; Amrouch, Mustapha

doi:10.1109/access.2023.3266093

Cited by 88 publications

(17 citation statements)

References 210 publications

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“…Tolstikhin et al [43] present an architecture based solely on multi-layer perceptrons (MLPs) for achieving competitive scores on image classification benchmarks. They demonstrate similar performance to attention-based net-works [44] without requiring them. In addition to introducing the new method proposed in this paper, a new synthetic dataset has been generated using Blender.…”

Section: Related Work and Contributionsmentioning

confidence: 78%

A Multilayer Perceptron-Based Spherical Visual Compass Using Global Features

Du,

Mateo,

Tahri

2024

Sensors

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This paper presents a visual compass method utilizing global features, specifically spherical moments. One of the primary challenges faced by photometric methods employing global features is the variation in the image caused by the appearance and disappearance of regions within the camera’s field of view as it moves. Additionally, modeling the impact of translational motion on the values of global features poses a significant challenge, as it is dependent on scene depths, particularly for non-planar scenes. To address these issues, this paper combines the utilization of image masks to mitigate abrupt changes in global feature values and the application of neural networks to tackle the modeling challenge posed by translational motion. By employing masks at various locations within the image, multiple estimations of rotation corresponding to the motion of each selected region can be obtained. Our contribution lies in offering a rapid method for implementing numerous masks on the image with real-time inference speed, rendering it suitable for embedded robot applications. Extensive experiments have been conducted on both real-world and synthetic datasets generated using Blender. The results obtained validate the accuracy, robustness, and real-time performance of the proposed method compared to a state-of-the-art method.

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Section: Related Work and Contributionsmentioning

confidence: 78%

A Multilayer Perceptron-Based Spherical Visual Compass Using Global Features

Du,

Mateo,

Tahri

2024

Sensors

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“…The objects count and their activity can be easily manipulated from the gathered results. The advancement of numerous techniques for object detection in various settings and applications demonstrates the progress and significance of object detection in research domains and its increased attention [10].…”

Section: Related Workmentioning

confidence: 99%

Multi-shift spatio-temporal features assisted deep neural network for detecting the intrusion of wild animals using surveillance cameras

Kumar,

Stanley

2024

E3S Web Conf.

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The coexistence of human populations and wildlife in shared habitats necessitates the development of effective intrusion detection systems to mitigate potential conflicts and promote harmonious relationships. Detecting the intrusion of wild animals, especially in areas where human-wildlife conflicts are common, is essential for both human and animal safety. Animal intrusion has become a serious threat to crop yield, impacting food security and reducing farmer profits. Rural residents and forestry workers are increasingly concerned about the issue of animal assaults. Drones and surveillance cam-eras are frequently used to monitor the movements of wild animals. To identify the type of animal, track its movement, and provide its position, an effective model is needed. This paper presents a novel methodology for detecting the intrusion of wild animals using deep neural networks with multishift spatio-temporal features from surveillance camera video images. The pro-posed method consists of a multi-shift attention convolutional neural net-work model to extract spatial features, a multi-moment gated recurrent unit attention model to extract temporal features, and a feature fusion network to fully explore the spatial semantics and temporal features of surveillance video images. The proposed model was tested with images from three different datasets and achieved promising results in terms of mean accuracy and precision.

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“…One significant drawback is the need for more precise boundary delineation. Object detection focuses on identifying and localizing objects as a whole [71] without precisely outlining the contours of each object. In the case of the MC, which may have intricate and crucial anatomical details, the inability to delineate its boundaries precisely can limit the accuracy of subsequent analyses or measurements.…”

Section: ) Rfcnmentioning

confidence: 99%

Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

Farid Naufal,

Fatichah,

Renwi Astuti

et al. 2024

IEEE Access

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Context: Several recent studies in dentistry and maxillofacial imaging have concentrated on the mandibular canal (MC) segmentation in digital dental radiographs. In this research domain, deep learning approaches have demonstrated promising outcomes. Objective: This systematic literature review (SLR) aims to comprehensively analyze and synthesize the recent advancements in applying deep learning techniques for MC segmentation in digital dental radiographs. Method: This review encompasses studies published between 2018 and 2023, sourced from reputable databases, including PubMed, ScienceDirect, IEEE Xplore, and Google Scholar. Results: This study identified 30 primary research papers focusing on MC segmentation in digital dental radiographs. The review categorizes papers into two groups based on the digital dental radiograph type. The first group uses a 2D digital dental radiograph from a panoramic radiograph and 2D Cone Beam Computed Tomography (CBCT) scans. The second group uses 3D datasets from volumetric data from CBCT scans. Conclusion:The synthesized knowledge from this review is intended to guide researchers, dentists, and oral surgeons in leveraging deep learning advancements in MC segmentation for oral and maxillofacial surgery. Prior studies have faced challenges, including limited public datasets, variations in MC anatomy, time consumption and observer variability in MC annotation, complexities in deep learning models, and lack of practical implementation. To overcome these challenges, it is suggested that more public datasets be collaboratively created and shared within the research community, focusing on handling anatomy variability, improving digital dental radiograph quality, streamlining annotation processes through automated tools, and simplifying deep learning models for practical implementation.

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Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review

Cited by 88 publications

References 210 publications

A Multilayer Perceptron-Based Spherical Visual Compass Using Global Features

A Multilayer Perceptron-Based Spherical Visual Compass Using Global Features

Multi-shift spatio-temporal features assisted deep neural network for detecting the intrusion of wild animals using surveillance cameras

Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

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