Efficient and environmentally responsible tree planting is crucial to sustainable land management. Tree planting processes involve significant machinery and labor, impacting efficiency and ecosystem health. In response, Södra Skogsägarna introduced the BraSatt initiative to develop an autonomous planting vehicle called E-Beaver. This vehicle aims to simultaneously address efficiency and ecological concerns by autonomously planting saplings in clear-felled areas. BIT ADDICT, partnering with Södra Skogsägarna, is re- sponsible for developing the control system for E-Beaver’s autonomous navigation and perception.  In this thesis work, we examine the possibility of using the computer vision models YOLO and U-Net for detecting and segmenting newly planted saplings in a clear felled area. We also compare the models’ performances with and without augmenting the dataset to see if that would yield better-performing models. RGB and RGB-D images were gath- ered with the ZED 2i stereo camera. Two different models are presented, one for detecting saplings in RGB images taken with a top-down perspective and the other for segmenting saplings trunks from RGB-D images taken with a side perspective. The purpose of this the- sis work is to be able to use the models for evaluating the plating of newly planted saplings so that autonomous tree planting can be done.  The outcomes of this research showcase that YOLOv8s has great potential in detecting tree saplings from a top-down perspective and the YOLOv8s-seg models in segmenting sapling trunks. The YOLOv8s-seg models performed significantly better on segmenting the trunks compared to U-Net models.  The research contributes insights into using computer vision for efficient and ecologi- cally sound tree planting practices, poised to reshape the future of sustainable land man- agement. / BraSatt

In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. The development of convolutional neural networks, as a powerful tool, has contributed to solving classification or object detection tasks with the trend of larger and deeper models. It is hard to compare the models from the perspective of depth since they are of different structure. At the same time, semantic segmentation is computationally demanding for the reason that it requires classifying each pixel to certain classes. Running these complicated processes on resource-constrained embedded systems may cause performance degradation in terms of inference time and accuracy. Network pruning, a model compression technique, targeting to eliminate the redundant parameters in the models based on a certain evaluation rule, is one solution. Most traditional network pruning methods, structural or nonstructural, apply zero masks to cover the original parameters rather than literally eliminate the connections. A new pruning method, Torch-Pruning, has a general-purpose library for structural pruning. This method is based on the dependency between parameters and it can remove groups of less important parameters and reconstruct the new model. A cutting-edge research work towards solving several computer vision tasks, Yolov8 has proposed several pre-trained models from nano, small, medium to large and xlarge with similar structure but different parameters for different applications. This thesis applies Torch-Pruning to Yolov8 semantic segmentation models to compare the performance of pruning based on existing models with similar structures, thus it is meaningful to compare the depth of the model as a factor. Several configurations of the pruning have been explored. The results show that greater depth does not always lead to better performance. Besides, pruning can bring about more generalization ability for Gaussian noise at medium level, from 20% to 40% compared with the original models. / För att förstå miljöer från olika perspektiv har en mängd olika datorseendemetoder utvecklats för att upptäcka objekt, klassificera objekt eller till och med segmentera dem semantiskt. Semantisk segmentering växer i betydelse på grund av dess breda tillämpningar inom områden som robotik, miljöförståelse för virtuell eller förstärkt verklighet och autonom körning. Utvecklingen av konvolutionella neurala nätverk, som är ett kraftfullt verktyg, har bidragit till att lösa klassificerings- eller objektdetektionsuppgifter med en trend mot större och djupare modeller. Det är svårt att jämföra modeller från djupets perspektiv eftersom de har olika struktur. Samtidigt är semantisk segmentering beräkningsintensiv eftersom den kräver att varje pixel klassificeras till vissa klasser. Att köra dessa komplicerade processer på resursbegränsade inbäddade system kan orsaka prestandanedgång när det gäller inferenstid och noggrannhet. Nätverksbeskärning, en modellkomprimeringsteknik som syftar till att eliminera överflödiga parametrar i modellerna baserat på en viss utvärderingsregel, är en lösning. De flesta traditionella nätverksbeskärningsmetoder, både strukturella och icke-strukturella, tillämpar nollmasker för att täcka de ursprungliga parametrarna istället för att bokstavligen eliminera anslutningarna. En ny beskärningsmetod, Torch-Pruning, har en allmän användningsområde för strukturell beskärning. Denna metod är baserad på beroendet mellan parametrar och den kan ta bort grupper av mindre viktiga parametrar och återskapa den nya modellen. Ett banbrytande forskningsarbete för att lösa flera datorseenduppgifter, Yolov8, har föreslagit flera förtränade modeller från nano, liten, medium till stor och xstor med liknande struktur men olika parametrar för olika tillämpningar. Denna avhandling tillämpar Torch-Pruning på Yolov8 semantiska segmenteringsmodeller för att jämföra prestandan för beskärning baserad på befintliga modeller med liknande strukturer, vilket gör det meningsfullt att jämföra djupet som en faktor. Flera konfigurationer av beskärningen har utforskats. Resultaten visar att större djup inte alltid leder till bättre prestanda. Dessutom kan beskärning medföra en större generaliseringsförmåga för gaussiskt brus på medelnivå, från 20% till 40%, jämfört med de ursprungliga modellerna.

With the development of small satellites it has become easier and cheaper to deploy satellites for earth observation from space. While optical sensors capture high-resolution data, this data is traditionally sent to earth for analysis which puts a high constraint on the data link and increases the time for making data based decisions. This thesis explores the possibilities of deploying an AI model in small satellites for detecting objects in satellite imagery and therefore reduce the amount of data that needs to be transmitted. The neural network model YOLOv8 was trained on the xView and DIOR dataset and evaluated in a hardware restricted execution environment. The model achieved a mAP50 of 0.66 and could process satellite images at a speed of 309m2/s.

Autonomous cars have become a trending topic as cars become better and better at driving autonomously. One of the big changes that have allowed autonomous cars to progress is the improvements in machine learning. Machine learning has made autonomous cars able to detect and react to obstacles on the road in real time. Like in all machine learning, there exists no solution that works better than all others, each solution has different strengths and weaknesses. That is why this study has tried to find the strengths and weaknesses of the object detector You Only Look Once v8 (YOLOv8) in autonomous cars. YOLOv8 was tested for how fast and accurately it could detect pedestrians in traffic in normal daylight images and light-augmented images. The trained YOLOv8 model was able to learn to detect pedestrians at high accuracy on daylight images, with the model achieving a mean Average Precision 50 (mAP50) of 0.874 with a Frames per second (FPS) of 67. Finally, the model struggled especially when the images got darker which means that the YOLOv8 in the current stage might not be good as the main detector for autonomous cars, as the detector loses accuracy at night. More tests with other datasets are needed to find all strengths and weaknesses of YOLOv8. / Autonoma bilar har blivit ett trendigt ämne då bilar blir bättre och bättre på att köra självständigt. En av de stora förändringarna som har gjort det möjligt för autonoma bilar att utvecklas är framstegen inom maskininlärning. Maskininlärning har gjort att autonoma bilar kan upptäcka och reagera på hinder på vägen i realtid. Som i all maskininlärning finns det ingen lösning som fungerar bättre än alla andra, varje lösning har olika styrkor och svagheter. Det är därför den här studien har försökt hitta styrkorna och svagheterna hos objektdetektorn You Only Look Once v8 (YOLOv8) i autonoma bilar. YOLOv8 testades för hur snabbt och precist den kunde upptäcka fotgängare i bilder av trafiken i dagsljus och bilder där ljuset har förändrat. Den tränade YOLOv8-modellen kunde lära sig att upptäcka fotgängare med hög noggrannhet på bilder i dagsljus, där modellen uppnådde en genomsnittlig medelprecision 50 (mAP50) på 0,874 med en antal bilder per sekund (FPS) på 67. Modellen hade särskilt svårt när bilderna blev mörkare vilket gör att YOLOv8 i det aktuella stadiet kanske inte är tillräckligt bra som huvuddetektor för autonoma bilar, eftersom detektorn tappar noggrannhet på mörkare bilder. Fler tester med andra datauppsättningar behövs för att hitta alla styrkor och svagheter med YOLOv8.

This thesis addresses the problem of Road Damage Detection using object detection models,Yolov8 and Yolov5. While Yolov5 has been utilized in prior road damage detection projects, thiswork introduces the application of the newly released Yolov8 model to this domain. We haveprepared a dataset of 3,000 annotated images of road damage in Sweden and applied variousYolov8 and Yolov5 models to this dataset and a larger international one. The potential ofdeploying a lightweight Yolov8 model in a smartphone application for real-time detection, aswell as the effectiveness of an ensemble approach combining several models, were alsoexplored. The results show an F1 score of 0.57 and 0.6 for the best-performing models on theSwedish dataset and an international Road damage dataset respectively. Several box clusteringmethods were tested to combine the predictions of the ensemble, but none outperformed thebest individual model. A Quantized version of Yolov8 was deployed to a smartphone device withsatisfying performance. This work aims to create a model which can ultimately be used toimprove road safety and quality.T

<p>  </p> <p>The proper classification and disposal of waste are crucial in reducing environmental impacts and promoting sustainability. Several solid waste classification systems have been developed over the years, ranging from manual sorting to mechanical and automated sorting. Manual sorting is the oldest and most commonly used method, but it is time-consuming and labor-intensive. Mechanical sorting is a more efficient and cost-effective method, but it is not always accurate, and it requires constant maintenance. Automated sorting systems use different types of sensors and algorithms to classify waste, making them more accurate and efficient than manual and mechanical sorting systems. In this thesis, we propose the development of an intelligent solid waste detection, classification and tracking system using artificial deep learning techniques. To address the limited samples in the TrashNetV2 dataset and enhance model performance, a data augmentation process was implemented. This process aimed to prevent overfitting and mitigate data scarcity issues while improving the model's robustness. Various augmentation techniques were employed, including random rotation within a range of -20° to 20° to account for different orientations of the recycled materials. A random blur effect of up to 1.5 pixels was used to simulate slight variations in image quality that can arise during image acquisition. Horizontal and vertical flipping of images were applied randomly to accommodate potential variations in the appearance of recycled materials based on their orientation within the image. Additionally, the images were randomly scaled to 416 by 416 pixels, maintaining a consistent image size while increasing the dataset's overall size. Further variability was introduced through random cropping, with a minimum zoom level of 0% and a maximum zoom level of 25%. Lastly, hue variations within a range of -20° to 20° were randomly introduced to replicate lighting condition variations that may occur during image acquisition. These augmentation techniques collectively aimed to improve the dataset's diversity and the model's performance. In this study, YOLOv8, EfficientNet-B0 and VGG16 architectures were evaluated, and stochastic gradient descent (SGD) and Adam were used as the optimizer. Although, SGD provided better test accuracies compared to Adam. </p> <p>Among the three models, YOLOv8 showed the best performance, with the highest average precision mAP of 96.5%. YOLOv8 emerges as the top performer, with ROC values varying from 92.70% (Metal) to 98.40% (Cardboard). Therefore, the YOLOv8 model outperforms both VGG16 and EfficientNet in terms of ROC values and mAP. The findings demonstrate that our novel classifier tracker system made of YOLOv8, and supervision algorithms surpass conventional deep learning methods in terms of precision, resilience, and generalization ability. Our contribution to waste management is in the development and implementation of an intelligent solid waste detection, classification, and tracking system using computer vision and deep learning techniques. By utilizing computer vision and deep learning algorithms, our system can accurately detect, classify, and localize various types of solid waste on a moving conveyor, including cardboard, glass, metal, paper, and plastic. This can significantly improve the efficiency and accuracy of waste sorting processes.</p> <p>This research provides a promising solution for detection, classification, localization, and tracking of solid waste materials in real time system, which can be further integrated into existing waste management systems. Through comprehensive experimentation and analysis, we demonstrate the superiority of our approach over traditional methods, with higher accuracy and faster processing times. Our findings provide a compelling case for the implementation of intelligent solid waste sorting.</p>

Detecting failures in tricone drill bits is crucial in the mining industry due to their potential consequences, including operational losses, safety hazards, and delays in drilling operations. Timely identification of failures allows for proactive maintenance and necessary measures to ensure smooth drilling processes and minimize associated risks. Accurate failure detection helps mining operations avoid financial losses by preventing unplanned breakdowns, costly repairs, and extended downtime. Moreover, it optimizes operational efficiency by enabling timely maintenance interventions, extending the lifespan of drill bits, and minimizing disruptions. Failure detection also plays a critical role in ensuring the safety of personnel and equipment involved in drilling operations. Traditionally, failure detection in tricone drill bits relies on manual inspection, which can be time-consuming and labor-intensive. Incorporating artificial intelligence-based approaches can significantly enhance efficiency and accuracy. This thesis uses machine learning methods for failure inference in tricone drill bits. A classic Convolutional Neural Network (CNN) classification method was initially explored, but its performance was insufficient due to the small dataset size and imbalanced data. The problem was reformulated as an object detection task to overcome these limitations, and a post-processing operation was incorporated. Data augmentation techniques enhanced the training and evaluation datasets, improving failure detection accuracy. Experimental results highlighted the need for revising the initial CNN classification method, given the limitations of the small and imbalanced dataset. However, You Only Look Once (YOLO) algorithms such as YOLOv5 and YOLOv8 models exhibited improved performance. The post-processing operation further refined the results obtained from the YOLO algorithm, specifically YOLOv5 and YOLOv8 models. While YOLO provides bounding box coordinates and class labels, the post-processing step enhanced drill bit failure detection through various techniques such as confidence thresholding, etc. By effectively leveraging the YOLO-based models and incorporating post-processing, this research advances failure detection in tricone drill bits. These intelligent methods enable more precise and efficient detection, preventing operational losses and optimizing maintenance processes. The findings underscore the potential of machine learning techniques in the mining industry, particularly in mechanical drilling, driving progress and enhancing overall operational efficiency