Fig. 1. Flowchart of adaptive digital camouflage generation

Fig. 2. Data augmentation effect diagram. (a) Original image; (b) image with color transformation; (c) image with cropping and stitching; (d) image with occlusion transformation

Fig. 3. Target and mask. (a) Camouflage target; (b) target detection result; (c) camouflage target mask

Fig. 4. Impact of mask expansion coefficient on camouflage image quality evaluation metrics

Fig. 5. Impact of data augmentation on detection performance. (a) Collected image; (b) detection results with data augmentation; (c) detection results without data augmentation

Fig. 6. Object detection results. (a) Dry grass; (b) lush grass; (c) wooden texture; (d) urban ground

Fig. 7. Comparison of digital camouflage generated by different methods. (a) Target to be camouflaged; (b) watershed method; (c) greedy iterative method; (d) raster method; (e) Markov chain method; (f) pix2pix; (g) CycleGan; (h) proposed method

Fig. 8. Camouflage results in different environments. (a)(e) Dry grass and its camouflage result; (b)(f) lush grass and its camouflage result; (c)(g) wooden texture and its camouflage result; (d)(h) urban ground and its camouflage result

Fig. 9. Camouflage results in diverse environments. (a) Represent the captured images of different background environments; (b) the corresponding camouflage results

Fig. 10. Camouflage results for different target objects and backgrounds. (a) Different target objects and background environments; (b) corresponding camouflage results