Anchor Box Optimization for Object Detection
This addresses the need for more accurate and less labor-intensive anchor box design in object detection frameworks, though it is incremental as it builds on existing anchor-based methods.
The paper tackles the problem of heuristically pre-defined anchor boxes in object detection by proposing a method to dynamically learn anchor shapes, achieving consistent improvements of at least 1% mAP on benchmarks like Pascal VOC, MS COCO, and Brainwash.
In this paper, we propose a general approach to optimize anchor boxes for object detection. Nowadays, anchor boxes are widely adopted in state-of-the-art detection frameworks. However, these frameworks usually pre-define anchor box shapes in heuristic ways and fix the sizes during training. To improve the accuracy and reduce the effort of designing anchor boxes, we propose to dynamically learn the anchor shapes, which allows the anchors to automatically adapt to the data distribution and the network learning capability. The learning approach can be easily implemented with stochastic gradient descent and can be plugged into any anchor box-based detection framework. The extra training cost is almost negligible and it has no impact on the inference time or memory cost. Exhaustive experiments demonstrate that the proposed anchor optimization method consistently achieves significant improvement ($\ge 1\%$ mAP absolute gain) over the baseline methods on several benchmark datasets including Pascal VOC 07+12, MS COCO and Brainwash. Meanwhile, the robustness is also verified towards different anchor initialization methods and the number of anchor shapes, which greatly simplifies the problem of anchor box design.