Computer vision, a prominent subfield of artificial intelligence, has gained widespread util-ization in diverse domains such as surveillance, security, and robotics. This research en-deavors to develop an semi-automated defect detection system serving as a quality controlassurance mechanism for Nolato MediTor, a manufacturing company within the medicaldevice industries engaged in the production of anesthesia breathing bags. The primary fo-cus of this study revolves around the detection of a specific defect, namely, holes. Withinthe context of Nolato MediTor, prioritizing recall (sensitivity) assumes utmost signific-ance as it entails favoring the rejection of functional breathing bags over the inadvertentacceptance of defective ones. The proposed system encompasses a robust metallic standfacilitating precise positioning for three distinct camera angles, accompanied by a XiaomiRedmi Note 11 Pro phone and a software component, designed to process incoming imagefolders representing a complete view of a breathing bag from multiple angles. Subsequently,these images undergo analysis using the learned weights derived from the implementedMask R-CNN model, enabling a cohesive assessment of the breathing bag. The system’sperformance was rigorously evaluated, and the best-performing weights demonstrated aremarkable recall rate of 0.995 for the first test set, exceeding the desired recall thresholdof 95%. Similarly, for the second test set, the recall rate achieved an impressive value of0.949, narrowly missing the 95% threshold by a marginal 0.001. Furthermore, the com-putational efficiency, quantified as the processing time per breathing bag, on average, thelongest duration recorded amounted to approximately 10.151 seconds, with the poten-tial for further enhancement by employing a higher standard GPU. This study serves as aproof of concept, demonstrating the feasibility of achieving semi-automated quality controlutilizing CNN. The implemented system represents a promising prototype with potentialscalability for improved operational conditions and expanded defect coverage, thus pavingthe way towards a fully automated quality control within large-scale industries.

Le problème du dénombrement d’un grand nombre de très petits objets en mouvement dans les vidéos est un contexte applicatif jusqu’à présent peu étudié.Dans ce cadre, la difficulté réside essentiellement dans le fait qu’en raison de leurs très petites tailles apparentes dans la vidéo, il n’est pas possible de définir un modèle géométrique fiable de ces objets. Or, les travaux existants dans le domaine de la détection d’objets dans des vidéo, utilisent souvent un tel modèle géométrique des objets d’intérêt. Les méthodes de détection existantes ne sont de ce fait pas applicables directement dans le cadre de la détection de tels très petits objets. Dans le cadre de cette thèse, il est proposé une méthodologie complète permettant la détection de nombreux petits objets, avec un cadre applicatif visant plus particulièrement la détection et le comptage d’oiseaux migrateurs dans une vidéo. Le principe innovant, proposé en tant qu’une solution de ce problème, consiste à associer des coefficients de fiabilité de détection aux objets pour les dénombrer tout en évitant de prendre en compte de trop nombreuses fausses détections. Un algorithme hiérarchique analysant l’aspect spatio-temporel d’objets (leurs apparence et l’évolution dans le temps) dans une vidéo à l’aide de méthodes de traitement d’images, de statistique et de la logique floue est ainsi proposé. Le but des coefficients de fiabilité est d’estimer la probabilité que les paramètres d’une détection correspondent aux paramètres attendus pour les objets d’intérêt. Finalement, l’ensemble des coefficients est converti en une valeur qui évalue la séquence du traitement d’un objet. La somme de ces valeurs correspond au nombre d’objets d’intérêt dans une vidéo. Les résultats obtenus montrent que les bonnes détections sont pour la plupart comprises dans le dénombrement avec des coefficients de fiabilité égaux ou proche de 1, et où les fausses détections sont supprimées ou sous-pondérés avec des coefficients de fiabilité plus faible. Les résultats de comptage dans des vidéos contenant de très nombreux oiseaux sont proches de la vérité terrain, ce qui prouve la validité de la solution proposée comme un moyen de dénombrement automatique d’objets dans des vidéos. / The problem of counting of big volumes of very small moving objects in videos is a domain, which was not studied to date. The difficulty of this application consists essentially in the fact, that because of very small sizes of objects, apparent in the videos, it is impossible to define a reliable geometric model of these objects. The researches, existing in the domain of object detection in videos frequently use a geometrical model of objects of interest.For this reason, the existing methods of object detection cannot be applied for the detection of very small objects in the study case. This thesis proposes a complete methodology, allowing the detection of very small objects in videos, and designed particularly the detection and counting of migrating birds in videos. An innovative principle and the solution of this problem consist in association of coefficients of detection reliability to the objects, in order to count them, avoiding counting of many false detections. The solution proposes a hierarchical algorithm, which analyses the spatial and temporal aspects of objects (their appearance and evolution in time) in a video, by the means of methods of image processing, statistics, and fuzzy logic. The aim of the reliability coefficients is to estimate the probability, that the parameters of a detected objects conform to the expected parameters of the objects of interest. Finally, the coefficients are put together and converted into a value, which evaluates the sequence of processing, applied to detect an object. The sum of these values corresponds to the number of the objects of interest in a video. The results show, that the most of correct detections are characterized in the counting by the reliability coefficient equal or close to 1. The results show, that the most of correct detections have their reliability coefficients close to 1, and the false detection are deleted or have low reliability coefficients. The counting results in the videos with numerous groups of migrating birds are close to the ground trough. This validates the proposed solution as a method of automatic counting of objects in videos.

Designing cutting tools for the turning industry, providing optimal cutting parameters is of importance for both the client, and for the company's own research. By examining the metal chips that form in the turning process, operators can recommend optimal cutting parameters. Instead of doing manual classification of metal chips that come from the turning process, an automated approach of detecting chips and classification is preferred. This thesis aims to evaluate if such an approach is possible using either a Convolutional Neural Network (CNN) or a CNN feature extraction coupled with machine learning (ML). The thesis started with a research phase where we reviewed existing state of the art CNNs, image processing and ML algorithms. From the research, we implemented our own object detection algorithm, and we chose to implement two CNNs, AlexNet and VGG16. A third CNN was designed and implemented with our specific task in mind. The three models were tested against each other, both as standalone image classifiers and as a feature extractor coupled with a ML algorithm. Because the chips were inside a machine, different angles and light setup had to be tested to evaluate which setup provided the optimal image for classification. A top view of the cutting area was found to be the optimal angle with light focused on both below the cutting area, and in the chip disposal tray. The smaller proposed CNN with three convolutional layers, three pooling layers and two dense layers was found to rival both AlexNet and VGG16 in terms of both as a standalone classifier, and as a feature extractor. The proposed model was designed with a limited system in mind and is therefore more suited for those systems while still having a high accuracy. The classification accuracy of the proposed model as a standalone classifier was 92.03%. Compared to the state of the art classifier AlexNet which had an accuracy of 92.20%, and VGG16 which had an accuracy of 91.88%. When used as a feature extractor, all three models paired best with the Random Forest algorithm, but the accuracy between the feature extractors is not that significant. The proposed feature extractor combined with Random Forest had an accuracy of 82.56%, compared to AlexNet with an accuracy of 81.93%, and VGG16 with 79.14% accuracy. / DIGICOGS