Abstract Cockroaches are major urban pests with significant medical and economic impacts and are commonly used as indicators of poor sanitation. Conventional monitoring methods, which depend on manual trap inspection and counting, are labor-intensive and limit practicality for routine surveillance. This study incorporates computer vision into cockroach monitoring by developing a deep learning–based detection workflow using YOLO object detection models. Ninety-seven food premises across 4 areas—Gaya Street, Api-Api, KK Times Square, and Damai—were surveyed using baited sticky traps. Images of captured cockroaches were processed with YOLOv5, YOLOv8, and YOLOv12 for performance comparison, with YOLOv8 showing the highest accuracy and consistency; it was subsequently used for automated detection and counting. Four cockroach species were identified: the German cockroach (Blattella germanica), American cockroach (Periplaneta americana), Australian cockroach (Validiblatta australasiae), and brown-banded cockroach (Supella longipalpa), with B. germanica accounting for 95.17% of detections. Infestation rates were highest in KK Times Square and Gaya Street, areas with dense food operations and high tourism activity. The use of computer vision enabled rapid assessment of cockroach infestations and the creation of an infestation density heatmap for risk visualization to guide authorities in monitoring infestation status.
Ong et al. (Thu,) studied this question.