SCALE-AWARENESS FOR MORE ACCURATE OBJECT DETECTION USING MODIFIED SINGLE SHOT DETECTORS

Abstract

Object detection performance is directly related to the apparent size of the object to be detected, thus most state-of-the-art algorithms dedicate different detection heads for each object size. In this work, we propose an end-to-end pipeline to adapt a single-shot object detector (SSD) to the underlying object size distribution of the target detection domain. Our contributions are the adjustments to the detector architecture and the introduction of a novel batch sampling method. To validate the effect of our method, we chose a task-specific highly specialized object detection and classification dataset of tomato fruits that apart from bounding box information, it also contains class information for three ripening stages of each tomato fruit.More specifically, the major motivation and contributions are discussed in relation to the recent bibliography. Next, an extensive analysis of our pipeline is presented, where the concept of scale alignment is thoroughly presented along with the novel sampling method. Following the results of a series of experiments, we conclude that our pipeline significantly improves over the “off-the-shelf” base single-shot detector and its detection performance is comparable to more elaborate algorithms, especially if we measure detection performance slightly disregarding box localization. Lastly, we include a stratified ablation study in the closing sections where we measure the impact of each step along our proposed SSD adaptation pipeline.