A novel multiple targets detection method for service robots in the indoor complex scenes

With the expansive aging of global population, service robot with living assistance applied in indoor scenes will serve as a crucial role in the field of elderly care in the future. Service robots need to detect multiple targets when completing auxiliary tasks. However, indoor scenes are usually complex and there are many types of interference factors, leading to great challenges in the multiple targets detection. To overcome this technical difficulty, a novel improved Mask RCNN method for multiple targets detection in the indoor complex scenes is proposed in this paper. The improved model utilizes Mask RCNN as the network framework. On this basis, Convolutional Block Attention Module (CBAM) with channel mechanism and space mechanism is integrated, and the influence of different background, distance, angle and interference factors is comprehensively considered. Meanwhile, in order to evaluate the detection and identification effects of the established model, a comprehensive evaluation system based on loss function and Mean Average Precision (mAP) is established. For verification, experiments on the detection and identification effects under different distances, backgrounds, postures and interference factors were conducted. The results demonstrated that designed model improves the accuracy to a higher level and has a better anti-interference ability than other methods while the detection speed was nearly the same. This research will promote the application of intelligent service robots in the field of perception and target grasp.