This master's thesis deals with a vehicle detector based on the convolutional neural network and scene captured by drone. Dataset is described at the beginning, because the main aim of this thesis is to create practicly usable detector. Architectures of the forward neural networks which detector was created from are described in the next chapter. Techniques for building a detector based on the naive methods and current the most successful meta architectures follow the neural network architectures. An implementation of the detector is described in the second part of this thesis. The final detector was built on meta architecture Faster R-CNN and PVA neural network on which the detector achieved score over 90 % and 45 full HD frames per seconds.

Tato práce se zabývá detekcí chodců pomocí konvolučních neuronových sítí z pohledu autonomního vozidla. A to zejména jejich otestováním ve smyslu nalezení vhodné praxe tvorby datasetu pro machine learning modely. V práci bylo natrénováno celkem deset machine learning modelů meta architektur Faster R-CNN s ResNet 101 jako feature extraktorem a SSDLite s feature extraktorem MobileNet_v2. Tyto modely byly natrénovány na datasetech o různých velikostech. Nejlépší výsledky byly dosaženy na datasetu o velikosti 5000 snímků. Kromě těchto modelů byl vytvořen nový dataset zaměřující se na chodce v noci. Dále byla vytvořena knihovna Python funkcí pro práci s datasety a automatickou tvorbu datasetu.

The main aim of this master’s thesis is to get acquainted with the theory of the current segmentation methods, that use deep learning. Segmentation neural network that will be capable of segmenting individual instances of the objects will be proposed and created based on theoretical knowledge. The main focus of the segmentation neural network will be segmentation of electronic components from printed circuit boards.

In traditional industrial manufacturing, due to the limitations of science and technology, manual inspection methods are still used to detect product surface defects. This method is slow and inefficient due to manual limitations and backward technology. The aim of this thesis is to research whether it is possible to automate this using modern computer hardware and image classification of defects using different deep learning methods. The report concludes, based on results from controlled experiments, that it is possible to achieve a dice coefficient of more than 81%.

I projektet detekteras och följs granplantor i spatiospektrala bilder för att därefter skapa en hyperspektral datakub för av varje gran. För att detektera granarna prövas fyra metoder: manuell detektion, detektion med segmentering, detektion med SVM och detektion med neuralt nätverk. Minnesanvändning och körningstid jämförs mellan två implementationer, där hyperspektral rekonstruktion görs med olika metoder.

I projektet detekteras och följs granplantor i spatiospektrala bilder för att därefter skapa en hyperspektral datakub för av varje gran. För att detektera granarna prövas fyra metoder: manuell detektion, detektion med segmentering, detektion med SVM och detektion med neuralt nätverk. Minnesanvändning och körningstid jämförs mellan två implementationer, där hyperspektral rekonstruktion görs med olika metoder.

Pest inventory of a field is a way of knowing when the thresholds for pest controlis reached. It is of increasing interest to use machine learning to automate thisprocess, however, many challenges arise with detection of small insects both intraps and on plants.This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machinewere implemented. Trap detection with neural network models and a check sizefunction were tested for narrowing the detections down to pests of a certain size.The results indicates that with further refinement and more training images thisapproach might hold potential for fungus gnat and rape beetles.Further, this thesis also investigates detection performance of Mask R-CNNand YOLOv5 on different insects in fields for the purpose of automating thedata gathering process. The models showed promise for detection of rape beetles. YOLOv5 also showed promise as a multi-class detector of different insects,where sizes ranged from small rape beetles to larger bumblebees.

The automation in the aquaculture is proving to be more and more effective these days. The economic drain on the aquaculture farmers due to the high mortality of the shrimps can be reduced by ensuring the welfare of the animals. The health of shrimps can decline with even barest of changes in the conditions in the farm. This is the result of increase in stress. As shrimps are quite sensitive to the changes, even small changes can increase the stress in the animals which results in the decline of health. This severely dampens the mortality rate in the animals. Also, human interference while feeding the shrimps severely induces the stress on the shrimps and thereby affecting the shrimp’s mortality. So, to ensure the optimum efficiency of the farm, the feeding of the shrimps is made automated. The underfeeding and overfeeding also affects the growth of shrimps. To determine the right amount of food to provide for shrimps, Biomass is a very helpful parameter. The use of artificial intelligence (AI) to calculate the farm's biomass is the project's primary area of interest. This model uses the cameras mounted on top of the tank at densely populated areas. These cameras monitor the farm, and our model detects the biomass. By doing so, it is possible to estimate how much food should be distributed at that particular area. Biomass of the shrimps can be calculated with the help of the number of shrimps and the average lengths of the shrimps detected. With the reduced human interference in calculating the biomass, the health of the animals improves and thereby making the process sustainable and economical.

This paper concerns itself with object detection as a possible solution to Valmet’s quest for a visual-feedback system that can help operators and other personnel to more easily interact with their machines and equipment. New advancements in deep learning, specifically CNN models, have been exploring neural networks with detection-capabilities. Object detection has historically been mostly inaccessible to the industry due the complex solutions involving various tricky image processing algorithms. In that regard, deep learning offers a more easily accessible way to create scalable object detection solutions. This study has therefore chosen to review recent literature detailing detection models with a selective focus on factors making them realizable on ARM hardware and in turn mobile devices like phones. An attempt was made to single out the most lightweight and hardware efficient model and implement it as a prototype in order to help Valmet in their decision process around future object detection products. The survey led to the choice of a SSD-MobileNetsV2 detection architecture due to promising characteristics making it suitable for performance-constrained smartphones. This CNN model was implemented on Valmet’s phone of choice, Samsung Galaxy S8, and it successfully achieved object detection functionality. Evaluation shows a mean average precision of 60 % in detecting objects and a 4.7 FPS performance on the chosen phone model. TensorFlow was used for developing, training and evaluating the model. The report concludes with recommending Valmet to pursue solutions built on-top of these kinds of models and further wishes to express an optimistic outlook on this type of technology for the future. Realizing performance of this magnitude on a mid-tier phone using deep learning (which historically is very computationally intensive) sets us up for great strides with this type of technology in the future; and along with better smartphones, great benefits are expected to both industry and consumers. / Den här rapporten behandlar objekt detektering som en möjlig lösning på Valmets efterfrågan av ett visuellt återkopplingssystem som kan hjälpa operatörer och annan personal att lättare interagera med maskiner och utrustning. Nya framsteg inom djupinlärning har dem senaste åren möjliggjort framtagande av neurala nätverksarkitekturer med detekteringsförmågor. Då industrisektorn svårare tar till sig högst specialiserade algoritmer och komplexa bildbehandlingsmetoder (som tidigare varit fallet med objekt detektering) så ger djupinlärningsmetoder istället upphov till att skapa självlärande system som är återanpassningsbara och närmast intuitiva i dem fall där sådan teknologi åberopas. Den här studien har därför valt att studera ett par sådana teknologier för att hitta möjliga implementeringar som kan realiseras på något så enkelt som en mobiltelefon. Urvalet har därför bestått i att hitta detekteringsmodeller som är hårdvarumässigt resurssnåla och implementera ett sådant system för att agera prototyp och underlag till Valmets vidare diskussioner kring objekt-detekteringsslösningar. Studien valde att implementera en SSD-MobileNetsV2 modellarkitektur då den uppvisade lovande egenskaper kring hårdvarukraven. Modellen implementerades och utvärderades på Valmets mest förekommande telefon Samsung Galaxy S8 och resultatet visade på en god förmåga för modellen att detektera objekt. Den valda modellen gav 60 % precision på utvärderingsbilderna och lyckades nå 4.7 FPS på den implementerade telefonen. TensorFlow användes för programmering och som stödjande mjukvaruverktyg för träning, utvärdering samt vidare implementering. Studien påpekar optimistiska förväntningar av denna typ av teknologi; kombinerat med bättre smarttelefoner i framtiden kan det leda till revolutionerande lösningar för både industri och konsumenter.

Modern weeding is predominantly carried out by spraying whole fields with toxic pesticides, a process that accomplishes the main goal of eliminating weeds, but at a cost of the local environment. Weed management systems based on AI solutions enable more targeted actions, such as site-specific spraying, which is essential in reducing the need for chemicals. To introduce sustainable weeding in Swedish farmlands, we propose implementing a state-of-the-art Deep Learning (DL) algorithm capable of instance segmentation for remote sensing of weeds, before coupling an automated sprayer vehicle. Cereals have been chosen as the target crop in this study as they are among the most commonly cultivated plants in Northern Europe. We used Unmanned Aerial Vehicles (UAV) to capture images from several fields and trained a Mask R-CNN computer vision framework to accurately recognize and localize unique instances of weeds among plants. Moreover, we evaluated three different backbones (ResNet-50, ResNet101, ResNeXt-101) pre-trained on the MS COCO dataset and through transfer learning tuned the model towards our classification task. Some well-reported limitations in building an accurate model include occlusion among instances as well as the high similarity between weeds and crops. Our system handles these challenges fairly well. We achieved a precision of 0.82, recall of 0.61, and F1 score of 0.70. Still, improvements can be made in data preparation and pre-processing to further improve the recall rate. All and all, the main outcome of this study is the system pipeline which, together with post-processing using geographical field coordinates, could serve as a detector for half of the weeds in an end-to-end weed removal system. / Site-specific Weed Control in Swedish Agriculture