Detection And Counting Of E. Coli On Specialized Test Piece Using Yolo v4 - Mynn Wei, Teoh (Universiti Sains Malaysia, 2022)
Supplying clean, safe and drinkable water is still one of the on-going issues faced by the world. To date, people around the still contract sickness and diseases related to unsanitary water. One of the most common sicknesses is diarrhoea and the main contributor to it is Escherichia Coli or in short, E. Coli. E. Coli is a bacterium commonly found in environment and if consumed in moderate and high amounts, may lead to critical illness and death. Therefore, there is a dire need for vision automation in detection of E. Coli bacteria. To date, the process of identify the quality of water is still not accurate and is time consuming. The quality of water is measured by colony forming unit per 100mL or in short CFUs/100mL. Only counting of the colonies is possible to obtain that desired value, which even until today, is still counted by sight. This leads to inaccurate E. Coli colony reading and inappropriate water treatment procedures. The study includes the usage of machine learning capabilities to detect and count the colony present on the test piece. The sample images obtain from the laboratory is captured under ideal lighting condition and later augmentation process was carried out. The processed images are then annotated using Label Studio and later trained using YOLO v4, an object classifier network that employs Convolutional Neural Network (CNN). The network is being trained to pick up presence of E. Coli on the mentioned test piece and provide user the quality of water based on the CFU. The results showed that with only 50 test piece sample images, the model achieve a mAP accuracy of approximately 91%, IOU score of 0.82 and an average loss of 0.2588. During the test phase, this work recorded a precision of 0.9279 ± 0.04195, recall of 0.9474 ± 0.01831 and F-score of 0.9351 ± 0.02718. This research is the first step to automate the E. Coli detection and counting process and create a change to world.