Tiago Barros

Nuno Cunha, Tiago Barros, Mário Reis et al.

Multispectral imagery is frequently incorporated into agricultural tasks, providing valuable support for applications such as image segmentation, crop monitoring, field robotics, and yield estimation. From an image segmentation perspective, multispectral cameras can provide rich spectral information, helping with noise reduction and feature extraction. As such, this paper concentrates on the use of fusion approaches to enhance the segmentation process in agricultural applications. More specifically, in this work, we compare different fusion approaches by combining RGB and NDVI as inputs for crop row detection, which can be useful in autonomous robots operating in the field. The inputs are used individually as well as combined at different times of the process (early and late fusion) to perform classical and DL-based semantic segmentation. In this study, two agriculture-related datasets are subjected to analysis using both deep learning (DL)-based and classical segmentation methodologies. The experiments reveal that classical segmentation methods, utilizing techniques such as edge detection and thresholding, can effectively compete with DL-based algorithms, particularly in tasks requiring precise foreground-background separation. This suggests that traditional methods retain their efficacy in certain specialized applications within the agricultural domain. Moreover, among the fusion strategies examined, late fusion emerges as the most robust approach, demonstrating superiority in adaptability and effectiveness across varying segmentation scenarios. The dataset and code is available at https://github.com/Cybonic/MISAgriculture.git.

Diogo Mendonça, Tiago Barros, Cristiano Premebida et al.

Autonomous systems require robust Multi-Object Tracking (MOT) capabilities to operate reliably in dynamic environments. MOT ensures consistent object identity assignment and precise spatial delineation. Recent advances in foundation models, such as SAM2, have demonstrated strong zero-shot generalization for video segmentation, but their direct application to MOTS (MOT+Segmentation) remains limited by insufficient identity management and memory efficiency. This work introduces Seg2Track-SAM2, a framework that integrates pre-trained object detectors with SAM2 and a novel Seg2Track module to address track initialization, track management, and reinforcement. The proposed approach requires no fine-tuning and remains detector-agnostic. Experimental results on KITTI MOT and KITTI MOTS benchmarks show that Seg2Track-SAM2 achieves state-of-the-art (SOTA) performance, ranking fourth overall in both car and pedestrian classes on KITTI MOTS, while establishing a new benchmark in association accuracy (AssA). Furthermore, a sliding-window memory strategy reduces memory usage by up to 75% with negligible performance degradation, supporting deployment under resource constraints. These results confirm that Seg2Track-SAM2 advances MOTS by combining robust zero-shot tracking, enhanced identity preservation, and efficient memory utilization. The code is available at https://github.com/hcmr-lab/Seg2Track-SAM2

T. Barros, P. Conde, G. Gonçalves et al.

Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available at https://github.com/Cybonic/DL_vineyard_segmentation_study.git

Tiago Barros, Luís Garrote, Ricardo Pereira et al.

LiDAR-based place recognition is one of the key components of SLAM and global localization in autonomous vehicles and robotics applications. With the success of DL approaches in learning useful information from 3D LiDARs, place recognition has also benefited from this modality, which has led to higher re-localization and loop-closure detection performance, particularly, in environments with significant changing conditions. Despite the progress in this field, the extraction of proper and efficient descriptors from 3D LiDAR data that are invariant to changing conditions and orientation is still an unsolved challenge. To address this problem, this work proposes a novel 3D LiDAR-based deep learning network (named AttDLNet) that uses a range-based proxy representation for point clouds and an attention network with stacked attention layers to selectively focus on long-range context and inter-feature relationships. The proposed network is trained and validated on the KITTI dataset and an ablation study is presented to assess the novel attention network. Results show that adding attention to the network improves performance, leading to efficient loop closures, and outperforming an established 3D LiDAR-based place recognition approach. From the ablation study, results indicate that the middle encoder layers have the highest mean performance, while deeper layers are more robust to orientation change. The code is publicly available at https://github.com/Cybonic/AttDLNet

Ricardo Pereira, Luís Garrote, Tiago Barros et al.

Indoor scenes are usually characterized by scattered objects and their relationships, which turns the indoor scene classification task into a challenging computer vision task. Despite the significant performance boost in classification tasks achieved in recent years, provided by the use of deep-learning-based methods, limitations such as inter-category ambiguity and intra-category variation have been holding back their performance. To overcome such issues, gathering semantic information has been shown to be a promising source of information towards a more complete and discriminative feature representation of indoor scenes. Therefore, the work described in this paper uses both semantic information, obtained from object detection, and semantic segmentation techniques. While object detection techniques provide the 2D location of objects allowing to obtain spatial distributions between objects, semantic segmentation techniques provide pixel-level information that allows to obtain, at a pixel-level, a spatial distribution and shape-related features of the segmentation categories. Hence, a novel approach that uses a semantic segmentation mask to provide Hu-moments-based segmentation categories' shape characterization, designated by Segmentation-based Hu-Moments Features (SHMFs), is proposed. Moreover, a three-main-branch network, designated by GOS$^2$F$^2$App, that exploits deep-learning-based global features, object-based features, and semantic segmentation-based features is also proposed. GOS$^2$F$^2$App was evaluated in two indoor scene benchmark datasets: SUN RGB-D and NYU Depth V2, where, to the best of our knowledge, state-of-the-art results were achieved on both datasets, which present evidences of the effectiveness of the proposed approach.

Tiago Barros, Ricardo Pereira, Luís Garrote et al.

Autonomous Vehicles (AV) are becoming more capable of navigating in complex environments with dynamic and changing conditions. A key component that enables these intelligent vehicles to overcome such conditions and become more autonomous is the sophistication of the perception and localization systems. As part of the localization system, place recognition has benefited from recent developments in other perception tasks such as place categorization or object recognition, namely with the emergence of deep learning (DL) frameworks. This paper surveys recent approaches and methods used in place recognition, particularly those based on deep learning. The contributions of this work are twofold: surveying recent sensors such as 3D LiDARs and RADARs, applied in place recognition; and categorizing the various DL-based place recognition works into supervised, unsupervised, semi-supervised, parallel, and hierarchical categories. First, this survey introduces key place recognition concepts to contextualize the reader. Then, sensor characteristics are addressed. This survey proceeds by elaborating on the various DL-based works, presenting summaries for each framework. Some lessons learned from this survey include: the importance of NetVLAD for supervised end-to-end learning; the advantages of unsupervised approaches in place recognition, namely for cross-domain applications; or the increasing tendency of recent works to seek, not only for higher performance but also for higher efficiency.