In the recycling process of waste glass, all types of impurities at the front end of the glass are removed manually by sorting. The degree of automation is low. The research background for this study is the manual sorting scene of a glass recycling and processing firm in Shanghai. This paper proposes a method of pipeline impurity glass detection based on the YOLOv4 algorithm. Moreover, typical sample images were collected in the field as the dataset, and the category of thin-tape glass was defined by indirect detection of both ends. Additionally, the Kmeans++ algorithm was used to reset the previous boxes (anchors), and the feature fusion was performed using a high-resolution feature map with a four times down-sampling rate to improve the detection performance for small targets. The results show that the mean average precision (mAP) of the proposed model is 97.88%, and the average precision (AP) for rubber strip glass is 95.47%. Compared to other methods, number of parameters of the proposed model is reduced by 31.11%, the AP for rubber strip glass is increased by 6.70 percentage points, and the detection speed is 42.82 frame/s, which can meet the real-time demand. Therefore, the proposed detection method is suitable for the visual component of the automated task of sorting impurity glass.