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Object detection is crucial for real-world applications like the self-driving vehicle, search and rescue missions, and surveillance systems. Therefore, it is essential to accurately detect all objects in the field of view. While cutting-edge technologies like Mask R-CNN work in specific regions in images, therefore, some image regions are usually ignored one object is covered partially by the other. In our project, we improve the performance of object detection through a dual mechanism. In particular, our proposed framework removes the already-detected objects in the original image, then perform the detection process once again to force the attention to the ignorable regions. The final results are obtained by merging the two sets of detection results. We conduct experiments to demonstrate the effectiveness of the proposed framework.
Van Tam Nguyen
Primary Advisor's Department
Stander Symposium poster
"Improving Object Detection with Dual Mask R-CNN" (2019). Stander Symposium Posters. 1595.