Authors - Revathy P, Rakshana A, Tinu A V, Vijayakumar R Abstract - The increasing demand for efficient package delivery has led to a challenge in detecting food spills during transit. Traditional methods rely on manual inspection, which is time-consuming and prone to human error. This study proposes an AI-based approach using Convolutional Neural Networks (CNNs) implemented with TensorFlow to detect both damaged packages and spilled food packets. The model is trained on a large dataset of package and food packet images, learning key features indicative of physical damage and identifying food spills. By fine-tuning pre-trained CNN architectures, the model achieves high accuracy in detecting both damage and spills. The interface is attached with an alert mechanism that notifies when damage or spill is detected. The TensorFlow framework is used for building, training, and deploying the model efficiently. The proposed system aims to automate package and food packet inspection, reduce human labor, and improve delivery service reliability by providing fast and accurate damage and spill detection.
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