Anomaly Detection of Logistics Packages Based on CNN and Image Recognition Technology

To address abnormal situations such as packaging damage, liquid leakage, and item displacement during logistics package transportation, this paper introduces Convolutional Neural Network (CNN) and image recognition technology to achieve real-time anomaly detection and alarm, improving the response speed and accuracy of the logistics system. First, by designing multiple layers of convolution and pooling layers, adding Batch Normalization layers and Dropout layers to prevent overfitting, a CNN deep learning model suitable for logistics package anomaly detection is constructed. Key features are extracted through image preprocessing methods such as normalization and data enhancement to ensure that the model has high recognition adaptability to various abnormal situations such as damage and leakage. Secondly, through data enhancement methods such as rotation, scaling, brightness adjustment, and transfer learning using pre-trained models, the model's adaptability in different lighting, angles, and occlusion conditions is improved. Finally, the model is applied to actual monitoring, and real-time alarms are issued for abnormalities such as damage and leakage, which are then fed back to the system closed-loop management. This method is significantly better than traditional methods in terms of detection accuracy and response speed. Under natural light environment, the model detection accuracy is as high as 98.4%, reducing false positives and false negatives. Combining CNN's anomaly detection technology effectively improves the accuracy and efficiency of logistics monitoring and provides technical support for intelligent logistics management.