A multi-step proposal refinement network for improved instance localization and boundary refinement with an application to fish detection - Labao, Alfonso B.; Naval, Prospero C. (Quezon City College of Engineering, University of the Philippines Diliman, 2017)
We propose a deep learning model that implements a narrow multi-step proposal refining network (MPRN) to increase instance recall and improve horizontal and vertical boundary estimation. The MPRN's contribution is a network that learns an automatic correction mechanism from performing step-wise predictions of bounding box coordinates. Each step in the MPRN re-uses predicted box coordinates from previous steps to extract roi feature maps and abstract contextual information . This information is propagated to the succeeding step through skip-connections. As contextual features are gathered across the MPRN, later steps have more capacity to correctly predict objectness probabilities, and previously missed objects are re-detected in later steps. During training, gradient corrections for both feature maps and box coordinates are backpropagated in the MPRN from step 7 to step 1 through differentiable interpolation layers. By combining increased numbers of proposals with multi-step refinements, the model outperforms the weak label algorithm used for its training in terms of IoU precision I recall and boundary refinement. Its 7-step structure also has more object recall capacity than recent deep learning models with less steps. The model is tested in a fish localization task with 20-50 objects per frame, taken from ten (10) various underwater video sequences. Test set results show that the network has IoU recall increase of 25.44% from the weak label algorithm, and outperforms Faster R-CNN and a 2-step recursive model in terms of IoU and boundary accuracy metrics. Small objects with less than 30x30 pixels are located, and predicted bounding boxes for neighboring object instances are separated
Thesis (M.S. Computer Science)--University of the Philippines Diliman
Keywords: Computer vision; Fishes -- Detection; Object localization