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Abstract This paper proposes an object detection model named Feature Enriching Object Detection Framework with Weak Segmentation Loss (FEOD) based on convolutional neural networks, which is an improvement of You Only Look Once (YOLO). To overcome the shortcoming of insensitivity to small objects, a novel feature enriching module is proposed to augment the semantic information of the feature in the shallow layer within a typical deep detector. Meanwhile, Focal Loss is also introduced to our model to further improve the algorithm performance. To obtain features more suitable for object detection, a more powerful feature extractor – DetNet is used as the backbone. An mAP of 83.8 in VOC2007 test and an mAP of 82.1 in VOC2012 test are achieved with a FPS of 11.3 with a NVIDIA GTX1080Ti GPU. For a lower resolution version, we achieve an mAP of 81.9 and 80.9 respectively in VOC2007 and VOC2012 tests with FPS of 17. Comparisons with other object detection algorithms have shown that our method works well and achieves a trade-off of accuracy and speed. [ABSTRACT FROM AUTHOR]

Abstract Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated synthetic MSI in order to initialize a DCNN framework. We evaluate our network initialization scheme on the new RIT-18 dataset that we present in this paper. This dataset contains very-high resolution MSI collected by an unmanned aircraft system. The models initialized with synthetic imagery were less prone to over-fitting and provide a state-of-the-art baseline for future work. [ABSTRACT FROM AUTHOR]

Multi-label image classification is a challenging problem in computer vision. Motivated by the recent development in image classification performance using Deep Neural Networks, in this work, we propose a flexible deep Convolutional Neural Network (CNN) framework, called Local-Global-CNN (LGC), to improve multi-label image classification performance. LGC consists of firstly a local level multi-label classifier which takes object segment hypotheses as inputs to a local CNN. The output results of these local hypotheses are aggregated together with max-pooling and then re-weighted to consider the label co-occurrence or interdependencies information by using a graphical model in the label space. LGC also utilizes a global CNN that is trained by multi-label images to directly predict the multiple labels from the input. The predictions of local and global level classifiers are finally fused together to obtain MAP estimation of the final multi-label prediction. The above LGC framework could benefit from a pre-train process with a large-scale single-label image dataset, e.g., ImageNet. Experimental results have shown that the proposed framework could achieve promising performance on Pascal VOC2007 and VOC2012 multi-label image dataset. [ABSTRACT FROM AUTHOR]

Scene text detection and scene segmentation are meaningful tasks in the computer vision field. Could the semantic scene segmentation assist scene text detection in any degree? For example, can we expect the probability of a region being text is low if its surrounding segment, i.e., its context, is labeled as sky? In this paper, we have a positive answer by constructing a scene context-based text detection model. In this model, we use texton features and a fully-connected conditional random field (CRF) to estimate pixel-level scene class׳s probability to be considered as image׳s context feature. Meanwhile, maximally stable extremal regions (MSERs) are extracted, integrated and extended as image patches of character candidates. Then, each image patch is fed to a simple two-layer convolutional neural network (CNN) to automatically extract its character feature. The averaged context feature of the corresponding patch is considered as the patch׳s context feature. The character feature and context feature are fused as the input into a support vector machine for text/non-text determination. Finally, as a post-processing, neighboring text regions are grouped hierarchically. The performance evaluation on ICDAR2013 and SVT databases, as well as a preliminary evaluation on a patch-level database, proves that the scene context can improve the performance of scene text detection. Moreover, the comparative study with state-of-the-art methods shows the top-level performance of our method. [ABSTRACT FROM AUTHOR]

Enclosed spaces are common in built structures but pose a challenge to many forms of manual or robotic surveying and maintenance tasks. Part of this challenge is to train robot systems to understand their environment without human intervention. This paper presents a method to automatically classify features within a closed void using deep learning. Specifically, the paper considers a robot placed under floorboards for the purpose of autonomously surveying the underfloor void. The robot uses images captured using an RGB camera to identify regions such as floorboards, joists, air vents and pipework. The paper first presents a standard mask regions convolutional neural network approach, which gives modest performance. The method is then enhanced using a two-stage transfer learning approach with an existing dataset for interior scenes. The conclusion from this work is that, even with limited training data, it is possible to automatically detect many common features of such areas. • A method for semantic segmentation in closed spaces • Database of underfloor images are captured and segmented using a mask R-CNN. • Two-stage transfer learning and other optimisations presented to maximise accuracy • Case-study application to the detection of floorboards, joists, vents, pipes, etc for automated insulation spraying • Mapping of segmented data labels to 3D point cloud data [ABSTRACT FROM AUTHOR]