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The Three-River-Source region is a highly significant natural reserve inChina that harbors a plethora of botanical resources. To meet the practicalrequirements of botanical research and intelligent plant management, weconstruct 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 varyingshapes and sizes. The PTRS presents us with challenges such as dense occlusion,varying leaf resolutions, and high feature similarity among plants, promptingus to develop a novel object detection network named PlantDet. This networkemploys a window-based efficient self-attention module (ST block) to generaterobust feature representation at multiple scales, improving the detectionefficiency for small and densely-occluded objects. Our experimental resultsvalidate the efficacy of our proposed plant detection benchmark, with aprecision of 88.1%, a mean average precision (mAP) of 77.6%, and a higherrecall compared to the baseline. Additionally, our method effectively overcomesthe issue of missing small objects.

PlantDet: A benchmark for Plant Detection in the Three-Rivers-Source Region