Object Detection and Fruit Tree Growth Stage Identification Via YOLO with Inverted and Swin Transformer Blocks

Object detection remains a critical challenge in computer vision, demanding algorithms that are both fast and accurate. This research introduces YOLO-IST, a novel object detector that integrates YOLO, Inverted Blocks, and Swin Transformer. Two novel Inverted blocks are integrated into this architecture to enhance performance. To validate our approach, we construct a new fruit tree dataset for fruit growth stage detection and recognition. Furthermore, we evaluate our model on this newly constructed dataset and two standard benchmark datasets like Microsoft COCO 2017 and Pascal VOC, demonstrating its effectiveness. The code for our proposed YOLO-IST is available at GitHub: https://github.com/nutcliu2507/YOLO-IST.