Enhanced Object Detection Method with Decoupled Direction Prediction and Fusion Strategy

How to improve accuracy of location prediction and select optimal prediction anchor, which has been a great challenge in dense object detection studies and applications. Nowadays, most object detection algorithms obtain bboxes location prediction (x
1
,y
1
, x
2
,y
2
) by multiple convolution of regression features. Through experiments in this paper, we demonstrate that x-bounds (x
1
, x
2
) and y-bounds (y
1
, y
2
) of the bboxes location regression results are affected by different features with different significance. Based on our studies, we propose a new detector to decouple the regression features to obtain feat_x and feat_y, which are used to predict the x-bounds and y-bounds of the bboxes, respectively. Besides, our experiments also show that the point with the most accurate x-bounds prediction is often not the point with the most accurate y-bounds prediction. For this reason, we have incorporated two additional branches to assess the prediction quality of x-bounds and y-bounds respectively, while designing the novel target functions and the loss functions for each branch. In contrast to the traditional methods of all four bounds predicted by a single point, this paper combine the resulting x-bounds and y-bounds by self-invented combination strategy, and the optimal box location prediction can be obtained. The method proposed in this paper achieved an accuracy of 48.2% with backbone "ResNeXt-101-64×4d" in 24 epochs training, which is a 0.6% improvement compared to TOOD.