Han Ping, Zhuang Xujun, Zuo Huahong, Lou Ping, Chen Xiao
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Wuhan Chuyan Information Technology Co., Ltd., Wuhan 430030, China.
Sensors (Basel). 2023 Jul 11;23(14):6306. doi: 10.3390/s23146306.
Smart security based on object detection is one of the important applications of edge computing in IoT. Anchors in object detection refer to points on the feature map, which can be used to generate anchor boxes and serve as training samples. Current object detection models do not consider the aspect ratio of the ground-truth boxes in anchor assignment and are not well-adapted to objects with very different shapes. Therefore, this paper proposes the Lightweight Anchor Dynamic Assignment algorithm (LADA) for object detection. LADA does not change the structure of the original detection model; first, it selects an equal proportional center region based on the aspect ratio of the ground-truth box, then calculates the combined loss of anchors, and finally divides the positive and negative samples more efficiently by dynamic loss threshold without additional models. The algorithm solves the problems of poor adaptability and difficulty in the selection of the best positive samples based on IoU assignment, and the sample assignment for eccentric objects and objects with different aspect ratios was more reasonable. Compared with existing sample assignment algorithms, the LADA algorithm outperforms the MS COCO dataset by 1.66% over the AP of the baseline FCOS, and 0.76% and 0.24% over the AP of the ATSS algorithm and the PAA algorithm, respectively, with the same model structure, which demonstrates the effectiveness of the LADA algorithm.
基于目标检测的智能安全是边缘计算在物联网中的重要应用之一。目标检测中的锚点是指特征图上的点，可用于生成锚框并作为训练样本。当前的目标检测模型在锚点分配中没有考虑真实框的宽高比，对形状差异很大的物体适应性不佳。因此，本文提出了用于目标检测的轻量级锚点动态分配算法（LADA）。LADA不改变原始检测模型的结构；首先，它根据真实框的宽高比选择一个等比例的中心区域，然后计算锚点的组合损失，最后通过动态损失阈值更高效地划分正负样本，无需额外的模型。该算法解决了基于交并比（IoU）分配时适应性差以及最佳正样本选择困难的问题，并且对偏心物体和不同宽高比物体的样本分配更加合理。与现有样本分配算法相比，在相同模型结构下，LADA算法在基线FCOS的平均精度（AP）上比MS COCO数据集高出1.66%，在ATSS算法和PAA算法的AP上分别高出0.76%和0.24%，这证明了LADA算法的有效性。
