Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images

Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset

On Field Gesture-Based Robot-to-Robot Communication with NAO Soccer Players

Gesture-based communication is commonly used by soccer players during matches to exchange information with teammates. Among the possible forms of gesture-based interaction, hand signals are the most used. In this paper, we present a deep learning method for recognizing robot-to-robot hand signals exchanged during a soccer game. A neural network for estimating human body, face, hands, and foot position has been adapted for the application in the robot soccer scenario. Quantitative experiments carried out on NAO V6 robots demonstrate the effectiveness of the proposed approach. Source code and data used in this work are made publicly available for the community

Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming

An effective perception system is a fundamental component for farming robots, as it enables them to properly perceive the surrounding environment and to carry out targeted operations. The most recent methods make use of state-of-the-art machine learning techniques to learn a valid model for the target task. However, those techniques need a large amount of labeled data for training. A recent approach to deal with this issue is data augmentation through Generative Adversarial Networks (GANs), where entire synthetic scenes are added to the training data, thus enlarging and diversifying their informative content. In this work, we propose an alternative solution with respect to the common data augmentation methods, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with their synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared (NIR) information, generating four channel multi-spectral synthetic images. Quantitative experiments, carried out on three publicly available datasets, show that (i) our model is capable of generating realistic multi-spectral images of plants and (ii) the usage of such synthetic images in the training process improves the segmentation performance of state-of-the-art semantic segmentation convolutional networks

Weakly and semi-supervised detection, segmentation and tracking of table grapes with limited and noisy data

Detection, segmentation and tracking of fruits and vegetables are three fundamental tasks for precision agriculture, enabling robotic harvesting and yield estimation applications. However, modern algorithms are data hungry and it is not always possible to gather enough data to apply the best performing supervised approaches. Since data collection is an expensive and cumbersome task, the enabling technologies for using computer vision in agriculture are often out of reach for small businesses. Following previous work in this context (Ciarfuglia et al., 2022), where we proposed an initial weakly supervised solution to reduce the data needed to get state-of-the-art detection and segmentation in precision agriculture applications, here we improve that system and explore the problem of tracking fruits in orchards. We present the case of vineyards of table grapes in southern Lazio (Italy) since grapes are a difficult fruit to segment due to occlusion, colour and general illumination conditions. We consider the case in which there is some initial labelled data that could work as source data (e.g. wine grape data), but it is considerably different from the target data (e.g. table grape data). To improve detection and segmentation on the target data, we propose to train the segmentation algorithm with a weak bounding box label, while for tracking we leverage 3D Structure from Motion algorithms to generate new labels from already labelled samples. Finally, the two systems are combined in a full semi-supervised approach. Comparisons with state-of-the-art supervised solutions show how our methods are able to train new models that achieve high performances with few labelled images and with very simple labelling

Data Augmentation Using GANs for Crop/Weed Segmentation in Precision Farming

Farming robots need a fast and robust image segmentation module to apply targeted treatments, which require the ability to distinguish, in real time, between crop and weeds. Existing solutions make use of visual classifiers that are trained on large annotated datasets. However, generating large datasets with pixel-wise annotations is an extremely time-consuming task. In this work, we tackle the crop/weed segmentation problem by using a synthetic image generation method to augment the training dataset without the need of manually labelling the images. The proposed approach consists in training a Generative Adversarial Network (GAN), which can automatically generate realistic agricultural scenes. As a difference with respect to common GAN approaches, where the network learns how to reproduce an entire scene, we generate only instances of the objects of interest in the scene, namely crops. This allows to build a generative model that is more compact and easier to train. The generated objects are then placed into real images of agricultural datasets, thus creating new images that can be used for training. To evaluate the performance of the proposed approach, quantitative experiments have been carried out using different segmentation network architectures, showing that our method well generalizes across multiple architectures