Abstract
The automotive industry has an extremely high-quality product standard, not just for the security risks each faulty component can present, but the very brand image it must uphold at all times to stay competitive. In this paper, a prototype model is proposed for smart quality inspection using machine vision. The engine labels are detected using Faster-RCNN and YOLOv3 object detection algorithms. All the experiments were carried out using a custom dataset collected at an automotive assembly plant. Eight engine labels of two brands (Citroën and Peugeot) and more than ten models were detected. The results were evaluated using the metrics Intersection of Union (IoU), mean of Average Precision (mAP), Confusion Matrix, Precision and Recall. The results were validated in three folds. The models were trained using a custom dataset containing images and annotation files collected and prepared manually. Data Augmentation techniques were applied to increase the image diversity. The result without data augmentation was 92.5%, and with it the value was up-to 100%. Faster-RCNN has more accurate results compared to YOLOv3. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Nowadays, the robotic welding joint estimation, or weld seam tracking, has improved according to the new developments on computer vision technologies. Typically, the advances are focused on solving inaccurate procedures that advent from the manual positioning of the metal parts in welding workstations, especially in SMEs. Robotic arms, endowed with the appropriate perception capabilities, are a viable solution in this context, aiming for enhancing the production system agility whilst not increasing the production set-up time and costs. In this regard, this paper proposes a local perception pipeline to estimate joint welding points using small-sized/low-cost 3D cameras, following an eyes-on-hand approach. A metrological 3D camera comparison between Intel Realsene D435, D415, and ZED Mini is also discussed, proving that the proposed pipeline associated with standard commercial 3D cameras is viable for welding operations in an industrial environment.

Abstract
Industry 4.0 is changing the manufacturing paradigms across industries. However, many repetitive processes still rely heavily on human workers, as in the case of the automotive industry, where the final quality inspection of assembled vehicles is still performed using a paper-based conformity list. We instead propose a hybrid solution where a deep learning-based hierarchical autonomous detection system identifies the non-conforming parts and informs the operator via a wearable device, trained exclusively with simulated data. This scalable and cost-effective system achieved a 65.7% accuracy score, which, considering the experimental nature of this work, further confirms the potential of this approach.

Abstract
Analysis of locomotion is often used as a measure for impairment and recovery following experimental peripheral nerve injury. Compared to rodents, sheep offer several advantages for studying peripheral nerve regeneration. In the present study, we compared for the first time, two-dimensional (2D) and three-dimensional (3D) hindlimb kinematics during obstacle avoidance in the ovine model. This study obtained kinematic data to serve as a template for an objective assessment of the ankle joint motion in future studies of common peroneal nerve (CP) injury and repair in the ovine model. The strategy used by the sheep to bring the hindlimb over a moderately high obstacle, set to 10% of its hindlimb length, was pronounced knee, ankle and metatarsophalangeal flexion when approaching and clearing the obstacle. Despite the overall time course kinematic patterns about the hip, knee, ankle, and metatarsophalangeal were identical, we found significant differences between values of the 2D and 3D joint angular motion. Our results showed that the most apparent changes that occurred during the gait cycle were for the ankle (2D-measured STANCEmax: 157 +/- 2.4 degrees vs. 3D-measured STANCEmax: 151 +/- 1.2 degrees; P<.05) and metatarsophalangeal joints (2D-measured STANCEmin: 151 +/- 2.2 degrees vs. 3D-measured STANCEmin: 162 +/- 2.2 degrees; P<.01 and 2D-measured TO: 163 +/- 4.9 degrees vs. 3D-measured TO: 177 +/- 1.4 degrees; P<.05), whereas the hip and knee joints were much less affected. Data and techniques described here are useful for an objective assessment of altered gait after CP injury and repairin an ovine model.

Abstract
Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

Abstract
One of the principal complications of patients that suffer from Diabetes Mellitus (DM) and that can lead to ulceration is the Diabetic foot. As tissue inflammation causes temperature variation, several studies show that thermography can be used to detect complications in diabetic foot and help predicting the risk of ulceration. It is known that, although healthy individuals present characteristic plantar temperature variation patterns, the same does not happen with diabetic patients, for which a particular pattern can not be found; thus, making the measurement of the temperature variation more difficult. Given that, it is important to research in this field in order to obtain methods that can detect atypical variations of the temperature in the sole of the foot. With this in mind, the objective of this work is to present a methodology to analyze the distribution of temperature in thermograms of the foot's plant and classify it as belonging to a DM individual with risk of ulceration or a healthy individual. After foot partitioning with a clustering algorithm, basic statistical descriptors are computed for each cluster. A binary classifier to predict the risk of ulceration in the diabetic foot was evaluated with the different descriptors; both a quantitative temperature index and a classification threshold are calculated for each descriptor. To evaluate the performance of the classifier, experiments were conducted using a public dataset (containing 45 thermograms of healthy individuals and 122 images of DM ones); the following metrics were obtained: Accuracy = 78%, AUC = 86% and F-measure = 84%, with the best descriptor.