Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection
This work addresses a specific bottleneck in face detection training for computer vision applications, but it is incremental as it builds on existing R-CNN frameworks.
The paper tackles the problem of underutilized anchors in face detection training by proposing a novel training strategy, Precise Box Score (PBS), which uses anchors with IoUs between thresholds, and a model compression method, SEMCM, to improve performance; experimental results show consistent improvements in face detection.
For the training of face detection network based on R-CNN framework, anchors are assigned to be positive samples if intersection-over-unions (IoUs) with ground-truth are higher than the first threshold(such as 0.7); and to be negative samples if their IoUs are lower than the second threshold(such as 0.3). And the face detection model is trained by the above labels. However, anchors with IoU between first threshold and second threshold are not used. We propose a novel training strategy, Precise Box Score(PBS), to train object detection models. The proposed training strategy uses the anchors with IoUs between the first and second threshold, which can consistently improve the performance of face detection. Our proposed training strategy extracts more information from datasets, making better utilization of existing datasets. What's more, we also introduce a simple but effective model compression method(SEMCM), which can boost the performance of face detectors further. Experimental results show that the performance of face detection network can consistently be improved based on our proposed scheme.