A multi-step proposal refinement network for improved instance localization and boundary refinement with an application to fish detection - Labao, Alfonso B.
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