With the low-automation and stress problem of breeding white feather broiler, a non-contact weight estimation method combined with deep learning was proposed to estimate the weight of breeding white feather broilers quickly and accurately. Mask R-CNN and YOLACT (You only look at coefficients) was used to obtain the target mask and locate the target with position coordinate. The breeding white feather broilers can be completely stripped out from complex background. Then, the edge points of body were extracted for ellipse fitting, and the pixel body area can be obtained. Bivariate correlation analysis was used to show the significant correlation between body weight and body area which was linearly proportional to the pixel body area. The linear regression model between target pixel body area and body weight was established based on the least-square principle. The experimental results showed that the proposed method had a good effect. This method can accurately estimate the body weight of 28-week-old and 48-week-old breeding white feather broilers with different occasion, such as the ideal posture, the head extension, the head turning and partial occlusion. The average accuracy based on Mask R-CNN feature extraction was 97.23%, and the average accuracy based on YOLACT feature extraction was 97.49%. The lowest accuracy for single broiler in the group was 90.50%. The weight of breeding white feather broilers can be estimated quickly and accurately.