  The Three-River-Source region is a highly significant natural reserve in
China that harbors a plethora of botanical resources. To meet the practical
requirements of botanical research and intelligent plant management, we
construct a dataset for Plant detection in the Three-River-Source region
(PTRS). It comprises 21 types, 6965 high-resolution images of 2160*3840 pixels,
captured by diverse sensors and platforms, and featuring objects of varying
shapes and sizes. The PTRS presents us with challenges such as dense occlusion,
varying leaf resolutions, and high feature similarity among plants, prompting
us to develop a novel object detection network named PlantDet. This network
employs a window-based efficient self-attention module (ST block) to generate
robust feature representation at multiple scales, improving the detection
efficiency for small and densely-occluded objects. Our experimental results
validate the efficacy of our proposed plant detection benchmark, with a
precision of 88.1%, a mean average precision (mAP) of 77.6%, and a higher
recall compared to the baseline. Additionally, our method effectively overcomes
the issue of missing small objects.
