Fruit detection plays a vital role in robotic harvesting platforms. However, natural scene attributes such as illumination variation, branch and leaf occlusion, clusters of tomatoes, shading, etc. and double scene including image augmentation and natural scene have made fruit detection a difficult task.
An improved YOLOv3 model termed as Tomato detection models, which includes YOLODenseNet and YOLOMixNet was applied to solve these problems. YOLODenseNet incorporated DenseNet backbone, while the backbone of YOLOMixNet combined DarkNet and DenseNet. With the incorporation of spatial pyramid pooling (SPP), feature pyramid network (FPN), complete (CIoU) loss and Mish activation function into both models, the tested accuracy of YOLODenseNet at 98.3 % and YOLOMixNet at 98.4 % on natural scene performed better than YOLOv3 at 96.1 % and YOLOv4 at 97.6 %, but not with YOLOv4 under the double scene.
Furthermore, the obtained detection speed of YOLOMixNet at 47.4FPS was noted to be in close par with the YOLOv4 at 48.9FPS. Finally, the Tomato detection models showed reliability, better generalization, and a high prospect for real-time harvesting robots.
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Lawal, Olarewaju. (2021). Development of tomato detection model for robotic platform using deep learning. Multimedia Tools and Applications. 10.1007/s11042-021-10933-w.