Notice bibliographique
Résumé
Automated inspection systems have the potential to significantly improve quality and increase production rates in the manufacturing industry. Machine vision (MV) is an example of one inspection technology that has been successfully applied to production lines. A wide variety of industrial inspection applications of MV systems can be found in the literature. For example, Lee et al. (2007) applied a MV system to bird handling for the food industry. Reynolds et al. (2004) looked at solder paste inspection for the electronics industry. Gayubo et al. (2006) developed a system to locate tearing defects in sheet metal. There has also been numerous laboratory based work on MV systems with practical applications. Jackman et al. (2009) used a vision system to predict the quality of beef. Kumar (2003) worked on the detection of defects in twill weave fabric samples. Garcia et al. (2006) checked for missing and misaligned electronics components. Hunter et al. (1995) confirmed circularity in brake shoes. Kwak et al. (2000) identified surface defects in leather. Although the range of applications is broad, they all tended to adopt the same image processing system with four main stages. The first is image acquisition. This is followed by preprocessing of the image, including applying various filters and selecting regions of interest. The third stage is feature extraction where individual features are extracted from the image. Finally a classifier is used to determine whether a given part is acceptable or not. The automotive industry presents a particularly challenging environment for MV based inspection. With changing lighting conditions in a dirty environment there is a need for robust and accurate classifiers to perform accurate inspection. Feature selection routines can be used to improve the results of an ANFIS based classifier for automotive applications (Miles and Surgenor, 2009). Although there is potential for good results with this approach, it can take hours of processing time to compute an accurate solution (Killing et al., 2009). This chapter presents the results of a project where six classification techniques were examined to see if development time could be reduced without sacrificing performance. As a case study, the problem of fastener insertion to an automotive part known as a cross car beam was investigated. Images taken from a production assembly line were used as the source of the data. The types of classifiers under investigation were: 1) a Neural Network based processor, 2) Principle Component Analysis to reclassify the input feature set and 3) a direct Eigenimage approach to avoid the need to extract features from each image. These methods were compared in terms of classification accuracy. An additional data set was also used to test the performance of these classifiers in detecting orientation defects in addition to presence and absence of clips. The results of these investigations are presented with a comparison of the performance on different datasets.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».