  Tea chrysanthemum detection at its flowering stage is one of the key
components for selective chrysanthemum harvesting robot development. However,
it is a challenge to detect flowering chrysanthemums under unstructured field
environments given the variations on illumination, occlusion and object scale.
In this context, we propose a highly fused and lightweight deep learning
architecture based on YOLO for tea chrysanthemum detection (TC-YOLO). First, in
the backbone component and neck component, the method uses the Cross-Stage
Partially Dense Network (CSPDenseNet) as the main network, and embeds custom
feature fusion modules to guide the gradient flow. In the final head component,
the method combines the recursive feature pyramid (RFP) multiscale fusion
reflow structure and the Atrous Spatial Pyramid Pool (ASPP) module with cavity
convolution to achieve the detection task. The resulting model was tested on
300 field images, showing that under the NVIDIA Tesla P100 GPU environment, if
the inference speed is 47.23 FPS for each image (416 * 416), TC-YOLO can
achieve the average precision (AP) of 92.49% on our own tea chrysanthemum
dataset. In addition, this method (13.6M) can be deployed on a single mobile
GPU, and it could be further developed as a perception system for a selective
chrysanthemum harvesting robot in the future.
