计算机科学
稳健性(进化)
卷积神经网络
供应链
人工智能
供应链管理
智能交通系统
自动化
机器学习
系统工程
运输工程
工程类
机械工程
生物化学
化学
政治学
法学
基因
作者
Pramod Prakash JAGTAP,Satyen Kale,Rakesh Mahali
出处
期刊:SAE technical paper series
日期:2024-01-16
摘要
<div class="section abstract"><div class="htmlview paragraph">Efficient cargo transportation plays a crucial role in logistics management and supply chain operations. Accurately detecting and utilizing cargo space within vehicles is vital for maximizing transport capacity, minimizing costs, and optimizing resource allocation and time management. This research paper focuses on enhancing cargo utilization using intelligent systems to improve logistics management. The major research is on developing a system that combines computer vision algorithms and intelligent systems to detect and implement a combination of features for efficient use of cargo space within vehicles and monitor cargo to reduce losses. The proposed approach will use and utilize image processing methods to get relevant features and identify cargo areas. Machine learning models, such as convolutional neural networks (CNNs) and object detection frameworks, will be trained and evaluated on a comprehensive dataset of cargo images to identify the most effective approach for cargo space detection. The proposed research will involve collecting and analyzing relevant data, including vehicle dimensions, and cargo types. Various computer vision algorithms, such as object detection and machine learning algorithms, will be employed to accurately identify and quantify the available cargo space. Machine learning models, including deep learning frameworks, will be trained and evaluated to improve the accuracy and robustness of the cargo space detection system. The outcomes of this research will provide valuable insights and practical solutions for logistics managers, transportation companies. By enhancing cargo space utilization by observing different parameters, transportation efficiency can be improved, leading to reduced costs, optimized resource utilization, and enhanced overall logistics management.</div></div>
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