YOLOFig detection model development using deep learning

Abstract The detection of fruit, including accuracy and speed is of great significance for robotic harvesting. Nevertheless, attributes such as illumination variation, occlusion, and so on have made fruit detection a challenging task. A robust YOLOFig detection model was proposed to solve detection challenges and to improve detection accuracy and speed. The YOLOFig detection model incorporated Leaky activated ResNet43 backbone with a new 2,3,4,3,2 residual block arrangement, spatial pyramid pooling network (SPPNet), feature pyramid network (FPN), complete (CIoU) loss, and distance DIoU−NMS to improve the fruit detection performance. The obtained average precision (AP) and speed (frames per second or fps) respectively under 2,3,4,3,2 residual block arranged backbone for YOLOv3b is 78.6% and 69.8 fps, YOLOv4b is 87.6% and 57.1 fps, and YOLOFig is 89.3% and 96.8 fps; under 1,2,8,8,4 residual block arranged backbone for YOLOv3 is 77.1% and 56.3 fps, YOLOv4 is 87.1% and 52.5 fps, and YOLOResNet70 is 87.3% and 79 fps; and under 3,4,6,3 residual block arranged backbone for YOLOResNet50 is 85.4% and 77.1 fps. An indication that the new residual block arranged backbone of 2,3,4,3,2 outperformed 1,2,8,8,4 on an average AP of 1.33% and detection speed of 15.2%. Finally, the compared results showed that the YOLOFig detection model performed better than other models at the same level of residual block arrangement. It can better generalize and is highly suitable for real‐time harvesting robots.