Scratch detection on car wheel covers using computer vision

Detekcija prask na platiščih avtomobilov s klasičnimi pristopi računalniškega vida ne daje dobrih rezultatov. Zato, ker so platišča različnih oblik, narejena iz različnih materialov in raznih barv. Tudi praske so različnih oblik, barv in velikosti, večrkrat tudi slabo vidne. Na platiščih se pojavlja tudi umazanija, ki dodatno ovira zaznavo. Zaradi tega je potrebna uporaba močnejših orodij. To so konvolucijska nevronska omrežja, ki doživljajo bliskovit razvoj nekaj zadnjih let. V diplomski nalogi analiziramo možnost zaznavanja prask na platiščih z globoko nevronsko mrežo. Za potrebe zaznave je potrebna segmentacija vsake točke vhodne slike. Ker so praske v primerjavi s celotnim platiščem majhne, smo se odločili uporabiti polno konvolucijsko omrežje U-Net. V namen diplome je bila pripravljena zbirka označenih fotografij, ki je lahko izhodišče za nadaljnje raziskave. Razvit model uspešno detektira praske, kljub mali učni množici. Na nevideni testni množici pripravljeni le za namen evalvacije je po metodi mIoU dosegel točnost 62,8,%. V primeru izboljšav in dodelav pa bi naš detektor prask bil primeren tudi za industrijsko rabo.Detection of scratches on car rims with classical computer vision approaches does not produce good results, because the rims are of different shapes, made of different materials and in different colors. Scratches are also in different shapes, colors and sizes. Scratches are often poorly visible and there is also dirt on rims, which further impedes visibility of them. This requires the use of more powerful tools, like convolutional neural networks that have been experiencing rapid development over the last few years. In thesis we analyze possibility of scratch detection on car rims with deep neural network. Segmentation of each point in the input image is required for detection purposes. Because the scratches are small compared to the entire region of the rim, we decided to use a fully convolutional network U-Net. For a purpose of the thesis, a collection of annotated pictures was prepared, which can be a starting point for further research. The developed model successfully detects scratches, despite the small learning set. On an unseen test set prepared for evaluation purposes only, it achieved 62.8,% accuracy using the mIoU method. With further improvements and refinements, our scratch detector would also be suitable for industrial use

Microstructure and Indentation Properties of Single-Roll and Twin-Roll Casting of a Quasicrystal-Forming Al-Mn-Cu-Be Alloy

In this investigation, strips of an experimental Al-Mn-Cu-Be alloy were manufactured by high-speed single-roll and twin-roll casting to stimulate the formation of a quasicrystalline phase during solidification. The strips were characterised by light microscopy, scanning and transmission electron microscopy, microchemical analysis, and X-ray diffraction. Indentation testing was used to determine the mechanical responses of the strips in different areas. A smooth surface was achieved on both sides of the twin-roll-cast strip, while the free surface of the single-roll-cast strip was rough. The microstructures in both strips consisted of an Al-rich solid solution matrix embedding several intermetallic phases Θ-Al2Cu, Be4Al (Mn, Cu), Al15Mn3Be2 and icosahedral quasicrystalline phase (IQC). The microstructure of the single-roll-cast strip was more uniform than that of the twin-roll-cast strip. Coarse Al15Mn3Be2 particles appeared in both alloys, especially at the centre of the twin-roll strip. These coarse particles adversely affected the strength and ductility. Nevertheless, both casting methods provided high-cooling rates, enabling the formation of metastable phases, such as quasicrystals. However, improvements in alloy composition and casting procedure are required to obtain enhanced microstructures and properties

Microstructure and Properties after Friction Stir Processing of Twin-Roll Cast Al–Mn–Cu–Be Alloy

We studied the effect of friction stir processing (FSP) on the microstructure and properties of high-speed twin-roll cast strips made of an experimental Al–Mn–Cu–Be alloy. The samples were examined using light, scanning, and transmission electron microscopy, microchemical analysis, X-ray diffraction, and indentation testing. During FSP, the rotational speed varied, while other parameters remained constant. The uniformity of the microstructure increased with the growing rotational speed. In the stir zone, several processes took place, and the most important were: recrystallisation of the matrix grains, fragmentation of the primary intermetallic particles Al15Mn3Be2 and their more uniform distribution in the stir zone, fracture, and dispersion of the eutectic icosahedral quasicrystalline phase (IQC), transformation of tiny Al15Mn3Be2 and IQC particles into the τ1-Al26Mn6Cu4 phase and precipitation of Al–Mn–Cu precipitates. In the thermomechanically affected zone, new dislocations formed as well as dispersion of the IQC eutectic phase and recrystallisation of the matrix grains. In the heat-affected zone, dissolution of θ’-Al2Cu precipitates occurred. The hardness variation was not severe between the stir and heat-affected zone

Slovene translation of the SQuAD2.0 dataset

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. The English version of SQuAD2.0 was machine translated to Slovene, then the translation was manually reviewed and corrected where needed. The data is provided in JSON format and consists of a training set and a validation set