During the cigarette production process, the detection of holes in the filters presents an urgent challenge for manufacturers. Utilizing image sensors offers an effective solution to tackle this challenge effectively. While current detection devices mitigate this issue through the use of auxiliary light source technology and sophisticated deep learning image processing, they are often large-scale, stationary, and demand substantial computational resources. To overcome this limitation, a low-power battery-powered portable filter holes detection device is designed based on image processing to facilitate portability and sampling. Additionally, backlight transmission lighting components are employed to guarantee aperture definition after imaging. Furthermore, a filter holes recognition algorithm is proposed based on multi-threshold combination of computer vision (CV). The algorithm forms a plan view of the circumference of the cigarette by image stitching, then performs filter holes detection and filters the results. Experimental results show that operators can easily carry the device for sampling and testing, and can accurately perform filter holes detection, and the results provide valuable insights into the adjustment of system parameters in the cigarette production process. This device significantly aids in enhancing efficiency during the manual inspection processes both in the testing room and at the equipment stations.