Real-time detection of a camouflaged object in unstructured scenarios based on hierarchical aggregated attention lightweight network

Abstract

Accurate and real-time grasping of household items is a challenge for home service robots in complex indoor environments. In this paper, using transparent cups as the recognition object, an accurate, reliable, and real-time visual algorithm is proposed for the elderly who needs nursing care to solve the recognition problems of overlapping, blurred, and small cups in complex unstructured indoor scenes. In order to meet the lightweight deployment of home service robots while considering both high-scale and low-scale semantic features, this paper designs a multi-scale fusion lightweight backbone network based on the Split Shuffle Block (SSB) and Group Shuffle Block (GSB) feature encoding units. Among them, depthwise separable convolution (DwConv) uses to reduce the amount of coding unit parameters, and feature shuffle uses to promote information exchange and the ability of information expression. In order to accurately identify the transparent cup camouflaged in complex backgrounds, this paper proposed a lightweight feature enhancement module that combines multi-scale hierarchical aggregation attention and multi-branch parallel convolution structure. The proposed module used an adaptive weighting strategy and a channel normalization weighting strategy to highlight the active regions of boundary features in each branch feature map, enhance the exchange of boundary information, and reduce the loss of detailed information. The experimental results on the IndoorCup and MobileCup datasets show that the detection accuracy of the proposed method is 93.6% and 92.6%, respectively, and the model calculation amount is only 0.91 M, which can lightweight deploy on indoor mobile robots for real-time detection. From the results of qualitative comparisons, the proposed method has strong robustness. It can effectively suppress false and missed detection caused by background interference. Likewise, it also effectively identifies camouflage objects and small objects in complex backgrounds.