LA-U2Net: location-aware U2Net for salient object detection
Xinliang Huang, JIAXIN LI, Yan Fang Ding, Pengfei Liu, WEIDONG LIANG, XIUJUAN ZHU · Eighth Symposium on Novel Photoelectronic Detection Technology and Applications · 2022
Salient object detection(SOD) is particularly important especially for applications like autonomous driving which requires real-time inference speed and high performance. Most of the previous works however focus on global object accuracy but not on the connection of local objects. In this paper, we first process the cityscapes dataset into a saliency detection dataset, which focuses on distinguishing between moving objects on the road and moving objects on the sidewalk. In order to enable the saliency detection network to learn the connection between the target categories, we propose a gated convolution(GCov), which can control the input of the feature layer. For the evaluation of SOD, we combine a variety of loss functions to form a mixed loss. Equipped with the GCov and mixed loss, the proposed architecture is able to effectively distinguish the difference in the semantics of the location for the targets of the same category. Experimental results on the dataset show that our method has competitive results compared with other saliency detection networks.