Intellimice Classifier: Towards Smart Object Detection and Classification of Laboratory Mice Using Multi-Sensor Integration

Laboratory mice (Mus musculus) play a crucial role in scientific research, where accurate classification and sorting are essential for ensuring reliable experimental results. This study presents an intelligent multi-sensor system for the automated classification and sorting of laboratory mice based on three key parameters: health status, gender, and weight. The system integrates thermal imaging cameras AMG8833 for monitoring the health status of mice, object detection algorithms (YOLOv8) for gender classification, and load cell HX711 sensors for weight measurement. The integration of these sensors leverages advanced sensor fusion techniques to improve classification accuracy and efficiency. Thermal imaging detects physiological anomalies to assess the health condition of the mice, while object detection algorithms identify gender characteristics in real-time with high precision. Additionally, load cell sensors provide accurate weight data for further categorization. The combined system eliminates the need for manual intervention, ensuring a non-invasive, efficient, and scalable approach to laboratory animal management. The proposed system performed evaluation through multiple test scenarios aimed at assessing the health of mice and classifying their weight. The detection of mice gender was evaluated using a dataset comprising over 6,722 images stored in the STASRG laboratory. The test results indicated that the accuracy of animal sorting across three parameters achieved a 100% success rate. The accuracy of gender sorting was 86.67%, while the accuracy of weight measurement exhibited a difference of approximately 0.1 gram. The overall response time for sorting was 19 seconds. This multi-sensor integration demonstrates the potential to enhance laboratory workflows, minimize human error, and promote the welfare of laboratory animals via automated, data-driven processes.