Image Classification using Different Machine Learning Techniques DOI Creative Commons
Simegnew Yihunie Alaba

Published: July 10, 2023

<p>Artificial Neural Networks and Convolutional have become common tools for classification object detection, owing to their ability learn features without prior knowledge. During training, these networks the parameters, weights, biases. This paper proposes a simple Network (CNN) task. Furthermore, Bayesian neural network work is reproduced as baseline comparing my proposed networks. All experiments were conducted using MNIST dataset.</p> <p>While convolutional adjust parameters based on cost function during updates its backdrop that drives variational approximation true posterior. Hyperparameters such optimizer, learning rate, regularizers, dropout, epochs, etc., varied train two The achieved better accuracy, approximately 99\%, than previously implemented network. However, it difficult predict certainty of predictions made by networks, unlike learning, which makes easy do so. \href{https://github.com/Simeon340703/Classification_Networks}{You can find code this at}.</p>

Language: Английский

Wavelet Convolutions for Large Receptive Fields DOI
Shahaf E. Finder,

Roy Amoyal,

Eran Treister

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 363 - 380

Published: Oct. 30, 2024

Language: Английский

Citations

28
A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving DOI Creative Commons
Simegnew Yihunie Alaba, John Ball

Sensors, Journal Year: 2022, Volume and Issue: 22(24), P. 9577 - 9577

Published: Dec. 7, 2022

LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast decision-making when driving. The in the perception system, especially object detection, understand environment. Although 2D detection has succeeded during deep-learning era, lack of depth information limits understanding environment location. Three-dimensional sensors, such as LiDAR, give 3D about surrounding environment, which essential system. Despite attention computer vision community due multiple applications robotics driving, there are challenges, scale change, sparsity, uneven distribution data, occlusions. Different representations data methods minimize effect sparsity have been proposed. This survey presents LiDAR-based feature-extraction techniques data. coordinate systems differ camera datasets methods. Therefore, summarized. Then, state-of-the-art object-detection reviewed with selected comparison among

Language: Английский

Citations

57
Class-Aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset DOI Creative Commons
Simegnew Yihunie Alaba, M M Nabi, Chiranjibi Shah

et al.

Sensors, Journal Year: 2022, Volume and Issue: 22(21), P. 8268 - 8268

Published: Oct. 28, 2022

Fish species recognition is crucial to identifying the abundance of fish in a specific area, controlling production management, and monitoring ecosystem, especially endangered species, which makes accurate essential. In this work, problem formulated as an object detection model handle multiple single image, challenging classify using simple classification network. The proposed consists MobileNetv3-large VGG16 backbone networks SSD head. Moreover, class-aware loss function solve class imbalance our dataset. takes number instances each into account gives more weight those with smaller instances. This can be applied any or task imbalanced experimental result on large-scale reef dataset, SEAMAPD21, shows that improves over original by up 79.7%. Pascal VOC dataset also outperforms model.

Language: Английский

Citations

44
Deep Learning-Based Image 3-D Object Detection for Autonomous Driving: Review DOI
Simegnew Yihunie Alaba, John Ball

IEEE Sensors Journal, Journal Year: 2023, Volume and Issue: 23(4), P. 3378 - 3394

Published: Jan. 13, 2023

An accurate and robust perception system is key to understanding the driving environment of autonomous robots. Autonomous needs 3-D information about objects, including object’s location pose, understand clearly. A camera sensor widely used in because its richness color texture, low price. The major problem with lack information, which necessary environment. In addition, scale change occlusion make object detection more challenging. Many deep learning-based methods, such as depth estimation, have been developed solve information. This survey presents image bounding box encoding techniques evaluation metrics. image-based methods are categorized based on technique estimate an image’s insights added each method. Then, state-of-the-art (SOTA) monocular stereo camera-based summarized. We also compare performance selected models present challenges future directions detection.

Language: Английский

Citations

42
Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review DOI Creative Commons
Fahmida Islam, M M Nabi, John Ball

et al.

Sensors, Journal Year: 2022, Volume and Issue: 22(21), P. 8463 - 8463

Published: Nov. 3, 2022

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one them. In order safely navigate through any known or unknown environment, AGV must be able detect important elements on the path. Detection applicable both on-road and off-road, but they are much different in each environment. The key environment that identify drivable pathway whether there obstacles around it. Many works have been published focusing components various ways. this paper, a survey most recent advancements methods intended specifically for off-road has presented. For this, we divided literature into three major groups: positive negative obstacles. Each portion further multiple categories based technology used, example, single sensor-based, how data analyzed. Furthermore, added critical findings technology, challenges associated with possible future directions. Authors believe work will help reader finding who doing similar works.

Language: Английский

Citations

29
Vehicle Detection for Autonomous Driving: A Review of Algorithms and Datasets DOI
Jules Karangwa, Jun Liu, Zixuan Zeng

et al.

IEEE Transactions on Intelligent Transportation Systems, Journal Year: 2023, Volume and Issue: 24(11), P. 11568 - 11594

Published: July 27, 2023

Nowadays, vehicles with a high level of automation are being driven everywhere. With the apparent success autonomous driving technology, we keep working to achieve fully on roads. Efficient and accurate vehicle detection is one essential tasks in environment perception an vehicle. Therefore, numerous algorithms for have been developed. However, their strengths terms performance not deeply assessed or highlighted yet. This work comprehensively reviews existing methods datasets considering performances applications field driving. First, briefly describe tasks, evaluation criteria, public Second, provide rigorous review both classical latest methods, including machine vision-based, mmWave radar-based, LiDAR-based, sensor fusion-based methods. Finally, analyze pertinent challenges recommendations future works concerning detection. The present covers over 300 research aims help researchers interested driving, especially

Language: Английский

Citations

18
Faster region based convolution neural network with context iterative refinement for object detection DOI Creative Commons

Kishore Anthuvan Sahayaraj K.,

G. Balamurugan

Measurement Sensors, Journal Year: 2024, Volume and Issue: 31, P. 101025 - 101025

Published: Jan. 8, 2024

In this paper, proposed a novel method to improve the localisation precision of identified objects. We present framework for iteratively enhancing image region recommendations meet ground truth values in research. The Faster R–CNN (FR-CNN) seems be an object recognition deep convolutional network. It gives user impression that network is cohesive and single. can provide accurate timely predictions about whereabouts range first build unified model based on rapid relocate inaccurate area recommendations. Because emphasis detection, it may utilized with wide datasets compatible various FR-CNN architectures. Second, we focus application joint score function variety picture features. This depicts location concealed concerning other data updated structured production loss are only two inputs influence parameters scoring function. join-score iterative context refinement (CIR) used generate our final model, which then classified using Smooth Support Vector Machine (SSVM). measured accuracy mean average after training + CIR SSVM low-cost GPU PASCAL VOC 2012 dataset. Our results 3.6 % more exact than rival learning algorithms average.

Language: Английский

Citations

4
CycleGAN-Based Unsupervised Image Smoothing Framework with Wavelet Downsampling and Multi-Scale Spatially-Adaptive Attention DOI

Jiafu Zeng,

Huiyu Li, Yepeng Liu

et al.

Digital Signal Processing, Journal Year: 2025, Volume and Issue: 165, P. 105300 - 105300

Published: May 8, 2025

Language: Английский

Citations

0
Deep Learning-based Image 3D Object Detection for Autonomous Driving: Review DOI Creative Commons
Simegnew Yihunie Alaba, John Ball

Published: Aug. 11, 2022

<p>An accurate and robust perception system is key to understanding the driving environment of autonomous robots. Autonomous needs 3D information about objects, including object’s location pose, understand clearly. A camera sensor widely used in because its richness color, texture, low price. The major problem with lack information, which necessary environment. Additionally, scale change cclusion make object detection more challenging. Many deep learning-based methods, such as depth estimation, have been developed solve information. This survey presents image bounding box encoding techniques, feature extraction evaluation metrics detection. image-based methods are categorized based on technique estimate an image’s insights added each method. Then, state-of-the-art (SOTA) monocular stereo camera-based summarized. We also compare performance selected models present challenges future directions detection.</p>

Language: Английский

Citations

9
Active detection for fish species recognition in underwater environments DOI
Chiranjibi Shah, M M Nabi, Simegnew Yihunie Alaba

et al.

Published: June 6, 2024

Fish species must be identified for stock assessments, ecosystem monitoring, production management, and the conservation of endangered species. Implementing algorithms fish detection in underwater settings like Gulf Mexico poses a formidable challenge. Active learning, method that efficiently identifies informative samples annotation while staying within budget, has demonstrated its effectiveness context object recent times. In this study, we present an active model designed recognition environments. This can employed as system to effectively lower expense associated with manual annotation. It uses epistemic uncertainty Evidential Deep Learning (EDL) proposes novel module denoted Model Evidence Head (MEH) employs Hierarchical Uncertainty Aggregation (HUA) obtain informativeness image. We conducted experiments using fine-grained extensive dataset reef collected from Mexico, specifically Southeast Area Monitoring Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate framework achieves better performance on SEAMAPD21 demonstrating favorable balance between data efficiency recognition.

Language: Английский

Citations

1