Enhancing Cargo Transportation using Intelligent systems for better Logistics Management

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, and warehouse operators. 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. The integration of intelligent systems will enable real-time monitoring and decision support, further enhancing the agility and responsiveness of cargo transportation operations. Overall, this research aims to contribute to advancing cargo space detection techniques by controlling multiple parameters and promoting sustainable logistics practices.