Object detection is a fundamental task in computer vision. While approaches for axis-aligned bounding box detection have made substantial progress in recent years, they perform poorly on oriented objects which are common in several real-world scenarios such as aerial view imagery and security camera footage. In these cases, a large part of a predicted bounding box will, undesirably, cover non-object related areas. Therefore, oriented object detection has emerged with the aim of generalizing object detection to arbitrary orientations. This enables a tighter fit to oriented objects, leading to a better separation of bounding boxes especially in case of dense object distributions. The vast majority of the work in this area has focused on complex two-stage anchor-based approaches. Anchors act as priors on the bounding box shape and require attentive hyper-parameter fine-tuning on a per-dataset basis, increased model size, and come with computational overhead. In this work, we present DAFNe: A Dense one-stage Anchor-Free deep Network for oriented object detection. As a one-stage model, DAFNe performs predictions on a dense grid over the input image, being architecturally simpler and faster, as well as easier to optimize than its two-stage counterparts. Furthermore, as an anchor-free model, DAFNe reduces the prediction complexity by refraining from employing bounding box anchors. Moreover, we introduce an orientation-aware generalization of the center-ness function for arbitrarily oriented bounding boxes to down-weight low-quality predictions and a center-to-corner bounding box prediction strategy that improves object localization performance. DAFNe improves the prediction accuracy over the previous best one-stage anchor-free model results on DOTA 1.0 by 4.65% mAP, setting the new state-of-the-art results by achieving 76.95% mAP.
翻译:计算机视觉中的一项基本任务。 尽管轴对齐约束框探测方法近年来取得了显著进展, 但这些方法在多个真实世界情景中常见的定向物体上表现不佳, 比如空中视图图像和安全摄像片。 在这些情况下, 大部分预测的超参数绑定框将不可取地覆盖非目标相关区域。 因此, 定向物体探测方法已经出现, 目的是将物体探测普及到任意方向。 这样可以更严格地适应定向对象, 导致更严格地分解约束箱, 特别是在密集物体分布的情况下。 该地区绝大多数工作都集中在复杂的双级锚基做法上。 锁定器作为前身在捆绑定框形状上运行, 需要在每套数据的基础上对超参数进行仔细的微调。 因此, 定向物体探测已经出现, 旨在将物体探测目标推广到任意定向方向。 作为一阶段模型, DAFNE 进行更精确地对一个更精确的网络进行预测, 以更简单、更快速的方式, 将一个最精确的固定的轨道定位模型, 将一个更快速的轨道到更精确的轨道 。