The existing method based on convex hull theory has low detecting ratio and large locating error because of failing to extract effective contour of the concave regions for occluded fruits. In order to improve recognition accuracy and reduce error of the current method, a kind of improved algorithm for detecting and locating occluded Camellia oleifera fruits was proposed. Firstly, in order to get a grayscale image of occluded Camellia oleifera fruits, different color spaces of the original image were compared and then R—B chromatic aberration characteristic was chosen. The Otsu method was used to segment the grayscale image and the morphological operation was employed to remove residual noise, thus the regions of targets and backgrounds can be successfully separated by the algorithm. A kind of algorithm was used to extracte convex closure of each occluded regions and then the concave regions were obtained by subtracting the binary image from its convex closure image. The regions with pixels less than half of the biggest one in concave image were removed and the intersection points or concave points of occluded Camellia oleifera fruits were detected by a kind of concave point detection algorithm, then the occluded targets were separated by using Bresenham line drawing algorithm according to the intersection points. Convex closure of each separated regions was built and convex hull was extracted from it, after that a kind of ineffective contour removing algorithm was used to extracte effective contour that used to reconstruct the target contour from each convex hull. Contour reconstruction algorithm was used to rebuild the target contour of the occluded Camellia oleifera fruits based on the points of each corresponding effective contour, and then the reconstruction contour was merged that the distance between their centers was below the threshold value. In order to validate the performance of the improved algorithm, a comparative test was conducted, and the positioning errors were calculated. The test results showed that it needed 0.491 s to finish the recognition and location process in average by the proposed method, which accounted for only 2.46% of the total time-consuming for a single Camellia oleifera fruit by harvesting robot. Average recognition success rate of occluded Camellia oleifera fruits by the proposed method was 93.21%, which was 7.47 percentage points higher than that of the original method. Average segmentation error of the proposed method was 5.53%, which was reduced by 6.22 percentage points compared with that of the original method. Average overlap ratio of the proposed method was 93.43%, which was 6.79 percentage points less than the that of the traditional method. The test results indicated that the proposed method was feasible and effective to recognize and locate occluded Camellia oleifera fruits.