Deep Learning-based Image 3D Object Detection for Autonomous Driving: Review

Alaba, Simegnew

doi:10.36227/techrxiv.20442858

Cited by 12 publications

(2 citation statements)

References 96 publications

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“…However, single-stage models consider problems as regression tasks and learn class probabilities and bounding box information through a single pass to the network. 32 Single-stage models are faster than two-stage models; however, their performance is inferior. 33 Semi-supervised Object Detection: Different semi-supervised models have been developed to increase performance and decrease the effort of annotating data.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised learning for fish species recognition

Alaba

Shah

Nabi³

et al. 2023

Ocean Sensing and Monitoring XV

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Fish species recognition and detection are essential for fishery industries. Accurate and robust species classification and detection play a vital role in monitoring fish activities and identifying the distribution of a specific species, which is vital to know the endangered species. It is also essential for controlling production and overall ecosystem control and management. However, the role of current artificial intelligence technologies, such as deep learning, is limited in the ocean system compared to other areas like robotics and security. The major challenge in building a deep learning network is data availability, time, and cost of annotation and labeling. In this work, we build a semi-supervised deep-learning network to recognize fish species. The model is based on a student-teacher network where the teacher network generates pseudo-labels, and the student network is trained with the generated pseud-labels and the labeled data simultaneously. The student network updates the teacher network via an exponential moving average method. The model consists of a faster R-CNN with a feature pyramid network detector. The experimental result of the model on the challenging fish dataset shows a promising result for building semi-supervised object detection models.

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Section: Related Workmentioning

confidence: 99%

Semi-supervised learning for fish species recognition

Alaba

Shah

Nabi³

et al. 2023

Ocean Sensing and Monitoring XV

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“…In addition to the classification task, object detection works, such as [2], [3], [4], [5], [6], [7], [8], [9] used the convolutional network to get better performance over traditional machine learning. Various regularization and optimization techniques are used for the training.…”

Section: Introductionmentioning

confidence: 99%

Image Classification using Different Machine Learning Techniques

Alaba¹

2023

Preprint

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<p>Artificial Neural Networks and Convolutional Neural Networks are becoming common tools for classification and object detection tasks due to their power to learn features without prior knowledge. The networks learn the parameters, weights, and biases through training. This paper proposes a simple Neural Network and Convolutional Neural Network (CNN) to do a classification task. Additionally, the Bayesian neural network work is reproduced to compare the result to my proposed networks and used as a baseline for comparison. The MNIST dataset is used for all experiments. </p> <p>The simple neural networks and the convolutional networks adjust the parameters based on the cost function during training, whereas the Bayesian convolutional neural network updates the parameters based on the backdrop that drives a variational approximation to the true posterior. I trained the two proposed networks by varying the hyperparameters: optimizer, learning rate, regularizers, dropout, epochs, and others. My proposed work gives better classification accuracy, approximately 99 \%, than the previously implemented Bayesian convolutional neural network, but it is difficult to predict how certain the prediction is, which is easy for Bayesian learning. </p>

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