论文标题
动态框融合策略在对象检测中
Dynamic boxes fusion strategy in object detection
论文作者
论文摘要
微观场景上的对象检测是一项流行的任务。由于显微镜始终具有可变的宏伟速度，因此对象的规模可能有很大差异，从而负担探测器的优化。此外，相机聚焦的不同情况会带来模糊的图像，这带来了区分对象和背景之间边界的巨大挑战。 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％），这也是Chula-Parasiteegg-11数据集的最新方法。
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.
