Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Thu PM Pod J.8
Authors: Mai, Xiaochun; Zhang, Hong; Meng, Max Q.-H.
Title: Faster R-CNN with Classifier Fusion for Small Fruit Detection
Abstract:
The-state-of-the-art of fruit detection with Faster R-CNN shows lack of detection advantage on small fruits. One of reasons is only single level features is used for localization of proposal candidates. In this paper, we propose to incorporate a multiple classifier fusion strategy into a Faster R-CNN network for small fruit detection. We utilize features from three different levels to learn three classifiers for objectness classification in the stage of proposal localization. Probabilities from classifiers are combined by a simple convolutional layer to generate final objectness classification for proposal candidates. In order to keep diversity of multiple classifiers, a novel loss term of classifier correlation is introduced into original loss function. Experimental results show that our model is feasible for detecting small fruits.
ICRA 2018 Spotlight Video
Interactive Session Thu PM Pod J.8
Authors: Mai, Xiaochun; Zhang, Hong; Meng, Max Q.-H.
Title: Faster R-CNN with Classifier Fusion for Small Fruit Detection
Abstract:
The-state-of-the-art of fruit detection with Faster R-CNN shows lack of detection advantage on small fruits. One of reasons is only single level features is used for localization of proposal candidates. In this paper, we propose to incorporate a multiple classifier fusion strategy into a Faster R-CNN network for small fruit detection. We utilize features from three different levels to learn three classifiers for objectness classification in the stage of proposal localization. Probabilities from classifiers are combined by a simple convolutional layer to generate final objectness classification for proposal candidates. In order to keep diversity of multiple classifiers, a novel loss term of classifier correlation is introduced into original loss function. Experimental results show that our model is feasible for detecting small fruits.
