An Adaptive Sample Assignment Network for Tiny Object Detection
Honghao Dai, Shanshan Gao, Hong Huang, Deqian Mao, Chenhao Zhang, Yuanfeng Zhou
Abstract
Tiny objects often have a small proportion of pixels in the image, leading to significant differences in the number of positive and negative samples and the lack of feature information. Accurately determining the position and category of tiny objects remains a huge challenge for object detection research. Therefore, we design an Adaptive Sample Assignment Strategy(ASAS) and tiny object focusing enhancement module to solve the above two problems. Specifically, starting from the study of positive and negative sample selection and balance strategies for tiny objects, we construct a lightweight Object Existence Probability Determination Network (OEPD/Net) to focus on the areas where tiny objects exist, and achieve adaptive assignment and balance of samples. A top/down, layer by layer focusing enhancement module is designed to effectively enhance the propagation ability of high/level semantic information for tiny objects. The above two solutions have excellent generalization and migration capabilities and can be applied to any stage and two-stage object detection network, effectively enhancing TOD performance. Finally, this article provides a performance analysis of detection performance the detection network based on the OEPD/Net output results, and demonstrates the effectiveness of the proposed OEPD-Net and focusing enhancement module through extensive experiments on a public dataset.