Weeds are a significant threat to agricultural production. Weed classification systems based on image analysis have offered innovative solutions to agricultural problems, with convolutional neural networks (CNNs) playing a pivotal role in this task. However, CNNs are limited in their ability to capture global relationships in images due to their localized convolutional operation. Vision Transformers (ViT) and Pyramid Vision Transformers (PVT) have emerged as viable solutions to overcome this limitation. Our study aims to determine the effectiveness of CNN, PVT, and ViT in classifying weeds in image datasets. We also examine if combining these methods in an ensemble can enhance classification performance. Our tests were conducted on significant agricultural datasets, including DeepWeeds and CottonWeedID15. The results indicate that a maximum of 3 methods in an ensemble, with only 15 epochs in training, can achieve high accuracy rates of up to 99.17%. This study demonstrates that high accuracies can be achieved with ease of implementation and only a few epochs.

Shelf auditing holds significant importance within the retail industry’s industrial sector. It encompasses various processes carried out by human operators. This article aims to address the issue of identifying

outdated price tags on shelves, bridging the gap of an automated shelf audit. Our proposal introduces a minimum viable process that effectively detects, recognizes, and locates price tags using computer vision and deep learning techniques. The outcomes of this study demonstrate the robustness of our approach in generating a comprehensive list of price tags on shelves, which can be subsequently compared with a database to identify and flag obsolete ones.

This work aims to evaluate three strategies for analyzing clusters of ordinal categorical data (thematic maps) to support the territorial zoning of the Alto Taquari basin, MS/MT. We evaluated a model-based method, another based on the segmentation of the multi-way contingency table, and the last one based on the transformation of ordinal data into intervals and subsequent analysis of clusters from a proposed method of segmentation of the Self-Organizing Map after the neural network training process. The results showed the adequacy of the methods based on the Self-Organizen Map and the segmentation of the contingency table, as these techniques generated unimodal clusters with distinguishable groups.

The progress in computer vision has allowed the development of a diversity of precision agriculture systems, improving the efficiency and yield of several processes of farming. Among the different processes, crop monitoring has been extensively studied to decrease the resources consumed and increase the yield, where a myriad of computer vision strategies has been proposed for fruit analysis (e.g., fruit counting) or plant health estimation. Nevertheless, the problem of fruit ripeness estimation has received little attention, particularly when the fruits are still on the tree. As such, this paper introduces a strategy to estimate the maturation stage of fruits based on images acquired from handheld devices while the fruit is still on the tree. Our approach relies on an image segmentation strategy to crop and align fruit images, which a CNN subsequently processes to extract a compact visual descriptor of the fruit. A non-linear regression model is then used for learning a mapping between descriptors to a set of physicochemical parameters, acting as a proxy of the fruit maturation stage. The proposed method is robust to the variations in pose, lighting, and complex backgrounds, being ideal for working in the wild with minimal image acquisition constraints. Source code is available at https://anonymous.ciarp.

Non-referential Face Image Quality Assessment (FIQA) methods have gained popularity as a pre-filtering step in Face Recognition (FR) systems. In most of them, the quality score is usually designed with face comparison in mind. However, a small amount of work has been done on measuring their impact and usefulness on Presentation Attack Detection (PAD). In this paper, we study the effect of quality assessment methods on filtering bona fide and attack samples, their impact on PAD systems, and how the performance of such systems is improved when training on a filtered (by quality) dataset. On a Vision Transformer PAD algorithm, a reduction of 20\% of the training dataset by removing lower-quality samples allowed us to improve the Bona fide Presentation Classification Error Rate (BPCER) by 3\% in a cross-dataset test.

Object detection is a crucial task in autonomous driving, where domain shift between the training and the test set is one of the main reasons behind the poor performance of a detector when deployed. Some erroneous priors may be learned from the training set, therefore a model must be invariant to conditions that might promote such priors. To tackle this problem, we propose an adversarial learning framework consisting of an encoder, an object-detector, and a condition-classifier. The encoder is trained to deceive the condition-classifier and aid the object-detector as much as possible throughout the learning stage, in order to obtain highly discriminative features. Experiments showed that this framework is not very competitive regarding the trade-off between precision and recall, but it does improve the ability of the model to detect smaller objects and some object classes.

Over the years, many solutions have been suggested in order to improve object detection in maritime environments. However, none of these approaches uses flight information, such as altitude, camera angle, time of the day, and atmospheric conditions, to improve detection accuracy and network robustness, even though this information is often available and captured by the UAV when doing the surveillance.

This work aims to develop a network unaffected by image-capturing conditions, such as altitude and angle. To achieve this, metadata was integrated into the neural network, and an adversarial learning training approach was employed. This was built on top of the YOLOv7, which is a state-of-the-art real-time object detector.

To evaluate the effectiveness of this methodology, comprehensive experiments and analyses were conducted. Findings reveal that the improvements achieved by this approach are minimal when trying to create networks that generalize more across these specific domains. The YOLOv7 mosaic augmentation was identified as one potential responsible for this minimal impact because it also enhances the model's ability to become invariant to these image-capturing conditions. Another potential cause is the fact that the domains considered (altitude and angle) are not orthogonal with respect to their impact on captured images.

Further experiments should be conducted using datasets that offer more diverse metadata, such as adverse weather and sea conditions, which may be more representative of real maritime surveillance conditions.