Object detection algorithms like You Only Look Once (YOLOv4) can face challenges when multiple objects overlap within the same grid cell. In this scenario, accurately detecting and classifying each object becomes difficult. Data augmentation techniques can address this issue and improve the accuracy of YOLOv4. More diverse training data can be created by artificially generating images with non-overlapping objects through random shifting, rotating, resizing, color jittering, and flipping. This improves the robustness of the model and helps it better handle real-world images with diverse object configurations. Data augmentation and post-processing can help address overlapping objects in YOLOv4, improving accuracy and performance in object detection tasks. The network was trained to recognize 80 object classes and achieved a 99% prediction rate and 54% confidence rate.