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

Autonomous driving requires accurate, robust, and fast decision-making perception systems to understand the driving environment. Object detection is critical in allowing the perception system to understand the environment. The perception systems, especially 2D object detection and classification, have succeeded because of the emergence of deep learning (DL) in computer vision (CV) applications. However, 2D object detection lacks depth information, which is crucial to understanding the driving environment. Therefore, 3D object detection is fundamental for the perception system of autonomous driving and robotics applications to estimate the objects’ location and understand the driving environment. The CV community has been giving much attention recently to 3D object detection because of the growth of DL models and the need to know accurate locations of objects. However, 3D object detection is still challenging because of scale changes, the lack of 3D sensor information, and occlusions. Researchers have been using multiple sensors to solve these problems and further enhance the performance of the perception system. This survey presents the multisensor (camera, radar, and LiDAR) fusion-based 3D object detection methods. The fully autonomous vehicles need to be equipped with multiple sensors for robust and reliable driving. Camera, LiDAR, and radar sensors and their corresponding advantages and disadvantages are also presented. Then, relevant datasets are summarized, and state-of-the-art multisensor fusion-based methods are reviewed. Finally, challenges, open issues, and possible research directions are presented.

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