BBoxCut通过局部遮挡模拟提升小麦穗在遮挡下的检测精度
BBoxCut: A Targeted Data Augmentation Technique for Enhancing Wheat Head Detection Under Occlusions
- 用随机局部掩码模拟叶片和相邻麦穗遮挡，增强数据多样性
- 在三个检测器上分别提升mAP 1.9%~3.26%，尤其改善遮挡场景表现
- 适合农业视觉任务中需应对复杂田间遮挡的检测模型训练
小麦对全球粮食安全至关重要，是研究最广泛的作物之一。准确识别与测量小麦穗特征对育种选种意义重大，传统人工测量效率低。数字技术发展推动了自动化检测，但田间条件如叶片遮挡、麦穗重叠、光照变化和运动模糊带来挑战。本文提出新型数据增强方法BBoxCut，通过随机局部掩码模拟叶与邻近麦穗造成的遮挡。在三种先进目标检测器上评估，Faster R-CNN、FCOS和DETR的平均精度均值（mAP）分别提升2.76%、3.26%和1.9%。该方法在定性和定量层面均有显著改进，尤其在遮挡小麦穗场景下表现突出，验证了其在复杂田间条件下的鲁棒性。
原文摘要 · Abstract (English)
Wheat plays a critical role in global food security, making it one of the most extensively studied crops. Accurate identification and measurement of key characteristics of wheat heads are essential for breeders to select varieties for cross-breeding, with the goal of developing nutrient-dense, resilient, and sustainable cultivars. Traditionally, these measurements are performed manually, which is both time-consuming and inefficient. Advances in digital technologies have paved the way for automating this process. However, field conditions pose significant challenges, such as occlusions of leaves, overlapping wheat heads, varying lighting conditions, and motion blur. In this paper, we propose a novel data augmentation technique, BBoxCut, which uses random localized masking to simulate occlusions caused by leaves and neighboring wheat heads. We evaluated our approach using three state-of-the-art object detectors and observed mean average precision (mAP) gains of 2.76, 3.26, and 1.9 for Faster R-CNN, FCOS, and DETR, respectively. Our augmentation technique led to significant improvements both qualitatively and quantitatively. In particular, the improvements were particularly evident in scenarios involving occluded wheat heads, demonstrating the robustness of our method in challenging field conditions.
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