To enhance the accuracy of identifying and counting bulk damage points in crystals, this paper proposes an improved crystal damage detection (YOLOv8-OCD) algorithm. Initially, to address the nonuniform distribution of bulk damage points in crystals, a convolutional block attention module was introduced into the backbone network; therefore, the model focused on regions with dense bulk damage, improving feature extraction capabilities. Next, to handle the abundant small bulk damage points, a small target detection layer was designed to reduce the false-negative rate. Finally, considering the presence of low-quality instances in the dataset, a Wise-IoU loss function was used. Consequently, the model focused on instances with normal quality, enhancing the detection accuracy. Results demonstrated that compared with the baseline model, the improved model achieved an average precision of approximately 70%, which was an improvement of approximately 3 percentage points. Thus, the improved model balanced the detection accuracy and real-time performance. The effectiveness and advantages of this approach were verified through ablation experiments and comparisons.