Car depth estimation within a monocular image using a light CNN

Tighkhorshid, Amirhossein; Tousi, Seyed Mohamad Ali; Nikoofard, Amirhossein

doi:10.1007/s11227-023-05359-0

Cited by 6 publications

(2 citation statements)

References 45 publications

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“…Unsupervised learning uses unlabeled data to make predictions, while supervised learning builds a model using labeled data (target) to predict the output value for a new set of data. The most commonly used supervised algorithms are Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Discriminant Analysis (DA), Naive Bayes (NB), Random Forests (RFs), decision trees (DTs), and K-Nearest Neighbors (KNNs) [11][12][13]. The selection of a suitable ML algorithm is of prime importance in solving the difficult task of finding a solution to a given classification problem [14,15].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Mani,

Rakkiya Goundar Komarasamy,

Rajamanickam

et al. 2024

Processes

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Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification.

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Section: Introductionmentioning

confidence: 99%

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Mani,

Rakkiya Goundar Komarasamy,

Rajamanickam

et al. 2024

Processes

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“…Their research aimed to improve the efficiency of a faster R-CNN model by incorporating estimations of both the angle and distance of objects. Another study [21] employed a lightweight MTCNN for license plate detection and utilized an MLP to model the perspective relationship between license plate dimensions and depth. Although this approach primarily depends on license plate information, it can be useful as an auxiliary method for depth estimation.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach to Monocular Depth Estimation for Autonomous Vehicle Perception Systems

Afshar,

Shirmohammadi,

Ghahramani

et al. 2023

Sustainability

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Depth estimation is critical for autonomous vehicles (AVs) to perceive their surrounding environment. However, the majority of current approaches rely on costly sensors, making wide-scale deployment or integration with present-day transportation difficult. This issue highlights the camera as the most affordable and readily available sensor for AVs. To overcome this limitation, this paper uses monocular depth estimation as a low-cost, data-driven strategy for approximating depth from an RGB image. To achieve low complexity, we approximate the distance of vehicles within the frontal view in two stages: firstly, the YOLOv7 algorithm is utilized to detect vehicles and their front and rear lights; secondly, a nonlinear model maps this detection to the corresponding radial depth information. It is also demonstrated how the attention mechanism can be used to enhance detection precision. Our simulation results show an excellent blend of accuracy and speed, with the mean squared error converging to 0.1. The results of defined distance metrics on the KITTI dataset show that our approach is highly competitive with existing models and outperforms current state-of-the-art approaches that only use the detected vehicle’s height to determine depth.

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