Object detection on microscopic scenarios is a popular task. As microscopes always have variable magnifications, the object can vary substantially in scale, which burdens the optimization of detectors. Moreover, different situations of camera focusing bring in the blurry images, which leads to great challenge of distinguishing the boundaries between objects and background. To solve the two issues mentioned above, we provide bags of useful training strategies and extensive experiments on Chula-ParasiteEgg-11 dataset, bring non-negligible results on ICIP 2022 Challenge: Parasitic Egg Detection and Classification in Microscopic Images, further more, we propose a new box selection strategy and an improved boxes fusion method for multi-model ensemble, as a result our method wins 1st place(mIoU 95.28%, mF1Score 99.62%), which is also the state-of-the-art method on Chula-ParasiteEgg-11 dataset.
翻译:由于显微镜的物体探测总是有多种放大作用,该对象在规模上可能有很大差异,这给探测器的优化带来负担。此外,由于摄影机的集中情况不同,模糊的图像也带来了巨大的挑战,导致区分对象和背景之间的界限。为了解决上述两个问题,我们在Chula-ParasiteEgg-11数据集上提供一袋有用的培训战略和广泛的实验,带来关于ICIP 2022挑战的不可忽略的结果:微镜中的寄生蛋探测和分类,此外,我们提出了新的箱选择战略和改进的多模范聚合材料箱集成法,因此我们的方法赢得了第1个地方(moU 28%, mF1Score 99.62%),这也是Chula-ParasiteEgg-11数据集的先进方法。