The Vision Challenge Track 1 for Data-Effificient Defect Detection requires
competitors to instance segment 14 industrial inspection datasets in a
data-defificient setting. This report introduces the technical details of the
team Aoi-overfifitting-Team for this challenge. Our method focuses on the key
problem of segmentation quality of defect masks in scenarios with limited
training samples. Based on the Hybrid Task Cascade (HTC) instance segmentation
algorithm, we connect the transformer backbone (Swin-B) through composite
connections inspired by CBNetv2 to enhance the baseline results. Additionally,
we propose two model ensemble methods to further enhance the segmentation
effect: one incorporates semantic segmentation into instance segmentation,
while the other employs multi-instance segmentation fusion algorithms. Finally,
using multi-scale training and test-time augmentation (TTA), we achieve an
average mAP@0.50:0.95 of more than 48.49% and an average mAR@0.50:0.95 of
66.71% on the test set of the Data Effificient Defect Detection Challenge. The
code is available at this https URL

本研究介绍了 Aoi-overfifitting-Team 团队解决 Data-Efficient Defect Detection 的技术细节，基于 Hybrid Task Cascade 实例分割算法，借助 CBNetv2 强化基线结果，并提出了两种模型集成方法和多尺度训练及测试时间扩充技术，最终在测试集上实现超过 48.49% 的平均 mAP@0.50:0.95 和 66.71% 的平均 mAR@0.50:0.95。