Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

This paper describes preliminary work in the recent promising approach of generating synthetic training data for facilitating the learning procedure of deep learning (DL) models, with a focus on aerial photos produced by unmanned aerial vehicles (UAV). The general concept and methodology are described, and preliminary results are presented, based on a classification problem of fire identification in forests as well as a counting problem of estimating number of houses in urban areas. The proposed technique constitutes a new possibility for the DL community, especially related to UAV-based imagery analysis, with much potential, promising results, and unexplored ground for further research.Comment: Workshop on Deep-learning based computer vision for UAV in conjunction with CAIP 2019, Salerno, italy, September 201

Correction of arbitrary geometric artefacts in historical documents

The research presented in this thesis addresses the problem of correction of arbitrary geometric artefacts in historical documents. Geometric distortions in historical documents may be introduced at any time during the life cycle of a document, from when it was first printed to the time it is digitised by an imaging device. Such distortions appear as arbitrary warping, folds and page curl, and have detrimental effects to recognition (OCR) and readability (e.g. for print-on-demand). This thesis also critically examines the state of the art methods and identifies opportunities for significant improvement.Firstly, the present work focuses on the main issues in text line segmentation and proposes a method which is robust in the presence of various geometric distortions, other artefacts in historical documents, and dense and complex layout. Secondly, a precise base line detection method based on geometric features of the parametric model of the segmented line is presented. In other words, the proposed base line detection method not only takes into consideration unexpected geometric distortions, which are common in historical document images— but it also identifies certain main components of the text line, such as ascenders, descenders, and certain decorative marks, and makes intelligent distinctions between such native (but potentially misleading) components of the line and other global and local distortions of the whole page.Such precise derivation of the baselines (and in certain instances the top lines) will serve as building blocks for a major correction stage, namely the de-warping procedure. At its starting point, the proposed de-warping method takes into account both global and local characteristics of the text image and models the smooth deformations between text lines; by taking advantage of the proposed line segmentation and baseline detection stages, it can cope with a variety of distortions, such as page curl, arbitrary warping and fold, in a reliable, robust, and flexible manner

Restoration of arbitrarily warped historical document images using flow lines

Historical documents frequently suffer from arbitrary geometric distortions (warping and folds) due to storage conditions, use and to, some extent, the printing process of the time. In addition, page curl can be prominent due to the scanning technique used. Such distortions adversely affect OCR and print-on-demand quality. Previous approaches to geometric restoration either focus only on the correction of page curl or require supplementary informatio n obtained by additional scanning hardware ` not practical for existing scans. This paper presents a new approach to detect and restore arbitrary warping and folds, in addition to page curl. Warped text lines and the smooth deformation between them are precisely modelled as primary and secondary flow lines that are then restored to their original linear shape. Preliminary, but representative, experimental results, in comparison to a leading page curl removal method and an industry-standard commercial system, demonstrate the effectiveness of the proposed metho

Deep Learning with Data Augmentation for Fruit Counting

Counting the number of fruits in an image is important for orchard management, but is complex due to different challenging problems such as overlapping fruits and the difficulty to create large labeled datasets. In this paper, we propose the use of a data-augmentation technique that creates novel images by adding a number of manually cropped fruits to original images. This helps to increase the size of a dataset with new images containing more fruits and guarantees correct label information. Furthermore, two different approaches for fruit counting are compared: a holistic regression-based approach, and a detection-based approach. The regression-based approach has the advantage that it only needs as target value the number of fruits in an image compared to the detection-based approach where bounding boxes need to be specified. We combine both approaches with different deep convolutional neural network architectures and object-detection methods. We also introduce a new dataset of 1500 images named the Five-Tropical-Fruits dataset and perform experiments to evaluate the usefulness of augmenting the dataset for the different fruit-counting approaches. The results show that the regression-based approaches profit a lot from the data-augmentation method, whereas the detection-based approaches are not aided by data augmentation. Although one detection-based approach finally still works best, this comes with the cost of much more labeling effort.</p

Performance improvements of a sweet pepper harvesting robot in protected cropping environments

Using robots to harvest sweet peppers in protected cropping environments has remained unsolved despite considerable effort by the research community over several decades. In this paper, we present the robotic harvester, Harvey, designed for sweet peppers in protected cropping environments that achieved a 76.5% success rate on 68 fruit (within a modified scenario) which improves upon our prior work which achieved 58% on 24 fruit and related sweet pepper harvesting work which achieved 33% on 39 fruit (for their best tool in a modified scenario). This improvement was primarily achieved through the introduction of a novel peduncle segmentation system using an efficient deep convolutional neural network, in conjunction with three-dimensional postfiltering to detect the critical cutting location. We benchmark the peduncle segmentation against prior art demonstrating an improvement in performance with a (Formula presented.) score of 0.564 compared to 0.302. The robotic harvester uses a perception pipeline to detect a target sweet pepper and an appropriate grasp and cutting pose used to determine the trajectory of a multimodal harvesting tool to grasp the sweet pepper and cut it from the plant. A novel decoupling mechanism enables the gripping and cutting operations to be performed independently. We perform an in-depth analysis of the full robotic harvesting system to highlight bottlenecks and failure points that future work could address.</p