CNN-based object detection models that strike a balance between performance and speed have been gradually used in polyp detection tasks. Nevertheless, accurately locating polyps within complex colonoscopy video scenes remains challenging since existing methods ignore two key issues: intra-sequence distribution heterogeneity and precision-confidence discrepancy. To address these challenges, we propose a novel Temporal–Spatial self-correction detector (TSdetector), which first integrates temporal-level consistency learning and spatial-level reliability learning to detect objects continuously. Technically, we first propose a global temporal-aware convolution, assembling the preceding information to dynamically guide the current convolution kernel to focus on global features between sequences. In addition, we designed a hierarchical queue integration mechanism to combine multi-temporal features through a progressive accumulation manner, fully leveraging contextual consistency information together with retaining long-sequence-dependency features. Meanwhile, at the spatial level, we advance a position-aware clustering to explore the spatial relationships among candidate boxes for recalibrating prediction confidence adaptively, thus eliminating redundant bounding boxes efficiently. The experimental results on three publicly available polyp video dataset show that TSdetector achieves the highest polyp detection rate and outperforms other state-of-the-art methods. The code can be available at https://github.com/soleilssss/TSdetector.

中文翻译：

---

TSdetector：用于结肠镜视频检测的颞-空间自校正协作学习


基于 CNN 的对象检测模型在性能和速度之间取得平衡，已逐渐用于息肉检测任务。然而，在复杂的结肠镜检查视频场景中准确定位息肉仍然具有挑战性，因为现有方法忽略了两个关键问题：序列内分布异质性和精度置信度差异。为了应对这些挑战，我们提出了一种新的时间-空间自校正检测器 （TSdetector），它首先集成了时间级一致性学习和空间级可靠性学习，以连续检测目标。从技术上讲，我们首先提出了一种全局时间感知卷积，将前面的信息组装起来，动态引导当前的卷积核关注序列之间的全局特征。此外，我们设计了一种分层队列集成机制，通过渐进式积累方式组合多时间特征，充分利用上下文一致性信息，同时保留长序列依赖特征。同时，在空间层面，我们推进了位置感知聚类，探索候选框之间的空间关系，以自适应地重新校准预测置信度，从而有效地消除冗余的边界框。在三个公开的息肉视频数据集上的实验结果表明，TSdetector 实现了最高的息肉检出率，并且优于其他最先进的方法。该代码可在 https://github.com/soleilssss/TSdetector 获取。