To address the issue of low detection efficiency resulting from the limited field of view in oil abrasive particle image detection, a telecentric imaging-based method for abrasive particle image detection is proposed. The effects of field depth and magnification on channel depth, detection field of view, and pixel accuracy are examined using the principle of telecentric imaging. The traditional microscope lens is replaced with a telecentric lens, and a specialized microchannel structure for abrasive particle detection is designed. Further, we develop an online detection system for abrasive particle images using telecentric imaging, which ensures resolution while providing a larger field of view and channel depth, thereby enhancing detection efficiency. The collected images are preprocessed, and the abrasives are classified into four categories based on their morphological characteristics using the random forest (RF) algorithm: normal abrasives, fatigue abrasives, cutting abrasives, and spherical abrasives. The influence of the number of decision trees and features on the classification performance is investigated, and the parameters are optimized based on the findings. The experimental results indicate that the system can detect abrasive particles in a field of view of 2.1 mm×1.8 mm and a channel depth of 0.2 mm. The RF algorithm outperforms the K-nearest neighbor and support vector machine algorithms, achieving a classification accuracy of 93.75% for abrasive particles. This result validates the effectiveness and practicality of the proposed method, providing a novel solution for the online detection of abrasive particle images.