Amodal Instance Segmentation for Mealworm Growth Monitoring Using Synthetic Training Images

Automatic dimensioning of mealworms based on computer vision is challenging due to occlusions. Amodal instance segmentation (AIS) could be a viable solution, but the acquisition of annotated training data is difficult and time-consuming. This work proposes a new method to prepare data for training AIS models that reduces the human annotation effort significantly. Instead of acquiring the occluded images directly, only images of fully visible larvae are acquired and processed, allowing obtaining their contours via automatic segmentation. Next, synthetic images with occlusions are generated from the database of automatically extracted instances. The generation procedure uses simple computer graphics tools and is computationally inexpensive, yet yields images that allow training off-the-shelf AIS models. Since those models need to be tested on real data, which requires manual annotation, a data acquisition method that significantly simplifies the test set annotation process is demonstrated. Results are reported in terms of the amodal segmentation quality as well as the accuracy of larvae dimensioning, measured using the histogram intersection metric. The best-performing model achieves a mean average precision of 0.41 and a histogram intersection of 0.77, confirming the effectiveness of the proposed method of data acquisition and generation. The method is not specific to mealworm detection and could be applied to other similar problems where object occlusions pose a challenge.