Fig. 1. Block diagram of accurate target positioning

Fig. 2. Algorithm block diagram

Fig. 3. Human detection network model

Fig. 4. Dynamic variation of IOU at different scales

Fig. 5. Loss variation curve

Fig. 6. Recognition results of darknet. (a) Original border box; (b) body image pre-processed by border frame

Fig. 7. Segmentation effect diagrams of different iteration times. (a) Number of iteration is 4; (b) number of iteration is 5; (c) number of iteration is 10

Fig. 8. Comparison of human body effect for image 67-76 in dataset. (a1)-(j1) Proposed algorithm; (a2)-(j2) darknet only

Fig. 9. IOU contrast graph of three algorithms

Fig. 10. Image 91 segmentation effect diagram.(a)Original image segmentation of RGB image; (b) RGB image segmentation of boundary box coordinate processing detected by darknet; (c) after preprocessing the coordinates detected by darknet, the RGB map corresponds to the depth map segmentation

Fig. 11. Image 92 segmentation effect.(a)Original image segmentation of RGB image; (b)RGB image segmentation of boundary box coordinate processing detected by darknet; (c)after preprocessing the coordinates detected by darknet, the RGB map corresponds to the depth map segmentation

Fig. 12. Image 93 segmentation effect.(a)Original image segmentation of RGB image; (b)RGB image segmentation of boundary box coordinate processing detected by darknet; (c)after preprocessing the coordinates detected by darknet, the RGB map corresponds to the depth map segmentation