Fiber tension is a key constraint that affects carbon fiber product quality, and the accuracy of carbon fiber tow microtension measurement by machine vision inspection methods is limited due to model measurement limits. In this study, based on the existing machine vision tension measurement model, a method for the classification and detection of carbon fiber tow microtension through machine vision by introducing a neural network is proposed, whereby the ResNet18 model is improved according to fiber image features. The fiber image is classified into vibration and sag images. By combining the image classification results with image processing algorithms, the lower limit of the measurement of the lateral vibration model is obtained. The sagging string model is further introduced to estimate the fiber microtension of the fiber tow. The experimental results on the dataset show that compared with the support vector machine (SVM), traditional convolutional neural network (CNN), and ResNet18 model, the proposed method improves accuracy by 14.2, 9.2, and 10.5 percentage points, respectively, and the image recognition time is 3.5 ms. The method of determining segmented fiber tension using image classification extends the measurement lower limit of a single transverse vibration model. It reduces the relative error between the machine vision and tension sensor measurements to ±10% within the microtension interval of 1.5?3.0 cN, with a goodness-of-fit of 0.964 between the sag height of the carbon fiber tow and the actual tension. This method effectively improves the detection accuracy of microtension in carbon fiber tows.