This study explores the application of object detection models for detecting Arctic Foxes in camera trap images, a crucial step towards automating wildlife monitoring and enhancing conservation efforts. The study involved training models on You Only Look Once version 7(YOLOv7) architecture across different locations using k-fold cross-validation technique and evaluating their performance in terms of mean Average Precision (mAP), precision, and recall. The models were tested on both validation and unseen data to assess their accuracy and generalizability. The findings revealed that while certain models performed well on validation data, their effectiveness varied when applied to unseen data, with significant differences in performance across the datasets. While one of the datasets demonstrated the highest precision (88%), and recall (94%) on validation data, another one showed superior generalizability on unseen data (precision 76%, recall 95%). The models developed in this study can aid in the efficient identification of Arctic Foxes in diverse locations. However, the study also identifies limitations related to dataset diversity and environmental variability, suggesting the need for future research to focus on training models during different seasons and having different aged Arctic Foxes. Recommendations include expanding dataset diversity, exploring advanced object detection architectures to go one step further and detect Arctic Foxes with skin diseases, and testing the models in varied field conditions.

This master thesis investigates the possibility to create a machine vision solution for the automation of earth-moving machines. This research was done as without some type of vision system it will not be possible to create a fully autonomous earth moving machine that can safely be used around humans or other machines. Cameras were used as the primary sensors as they are cheap, provide high resolution and is the type of sensor that most closely mimic the human vision system. The purpose of this master thesis was to use existing real time object detectors together with transfer learning and examine if they can successfully be used to extract information in environments such as construction, forestry and mining. The amount of data needed to successfully train a real time object detector was also investigated. Furthermore, the thesis examines if there are specifically difficult situations for the defined object detector, how reliable the object detector is and finally how to use service-oriented architecture principles can be used to create deep learning systems. To investigate the questions formulated above, three data sets were created where different properties were varied. These properties were light conditions, ground material and dump truck orientation. The data sets were created using a toy dump truck together with a similarly sized wheel loader with a camera mounted on the roof of its cab. The first data set contained only indoor images where the dump truck was placed in different orientations but neither the light nor the ground material changed. The second data set contained images were the light source was kept constant, but the dump truck orientation and ground materials changed. The last data set contained images where all property were varied. The real time object detector YOLOv3 was used to examine how a real time object detector would perform depending on which one of the three data sets it was trained using. No matter the data set, it was possible to train a model to perform real time object detection. Using a Nvidia 980 TI the inference time of the model was around 22 ms, which is more than enough to be able to classify videos running at 30 fps. All three data sets converged to a training loss of around 0.10. The data set which contained more varied data, such as the data set where all properties were changed, performed considerably better reaching a validation loss of 0.164 compared to the indoor data set, containing the least varied data, only reached a validation loss of 0.257. The size of the data set was also a factor in the performance, however it was not as important as having varied data. The result also showed that all three data sets could reach a mAP score of around 0.98 using transfer learning.

Unmanned aerial vehicle (UAV) is an expanding interest in numerous industries for various applications. Increasing development of UAVs is happening worldwide, where various sensor attachments and functions are being added. The multi-function UAV can be used within areas where they have not been managed before. Because of their accessibility, cheap purchase, and easy-to-use, they replace expensive systems such as helicopters- and airplane-surveillance. UAV are also being applied into surveillance, combing object detection to video-surveillance and mobility to finding an object from the air without interfering with vehicles or humans ground. In this thesis, we solve the problem of using UAV on autonomous sites, finding an object and critical situation, support autonomous site operators with an extra safety layer from UAVs camera. After finding an object on such a site, uses GPS-coordinates from the UAV to see and place the detected object on the site onto a gridmap, leaving a coordinate-map to the operator to see where the objects are and see if the critical situation can occur. Directly under the object detection, reporting critical situations can be done because of safety-distance-circle leaving warnings if objects come to close to each other. However, the system itself only supports the operator with extra safety and warnings, leaving the operator with the choice of pressing emergency stop or not. Object detection uses You only look once (YOLO) as main object detection Neural Network (NN), mixed with edge-detection for gaining accuracy during bird-eye-views and motion-detection for supporting finding all object that is moving on-site, even if UAV cannot find all the objects on site. Result proofs that the UAV-surveillance on autonomous site is an excellent way to add extra safety on-site if the operator is out of focus or finding objects on-site before startup since the operator can fly the UAV around the site, leaving an extra-safety-layer of finding humans on-site before startup. Also, moving the UAV to a specific position, where extra safety is needed, informing the operator to limit autonomous vehicles speed around that area because of humans operation on site. The use of single object detection limits the effects but gathered object detection methods lead to a promising result while printing those objects onto a global positions system (GPS) map has proposed a new field to study. It leaves the operator with a viewable interface outside of object detection libraries.

This project aims to contribute to the discussion regarding reproducibility of machinelearning research. This is done through utilizing the methods specified in the report ImprovingReproducibility in Machine Learning Research [30] to select an appropriateobject detection machine learning research paper for reproduction. Furthermore, this reportwill explain fundamental concepts of object detection. The chosen machine learningresearch paper, You Only Look Once (YOLO) [40] is then explained, implemented andtrained with various hyperparameters and pre-processing steps.While the reproduction did not achieve the results presented by the original machinelearning paper, some key insights were established. Firstly, the results of the projectdemonstrates the importance of pretraining. Secondly, the checklist provided by the NeurIPS[30] should be adjusted such that it is applicable in more situations.

yes / Coinciding with advances in whole-slide imaging scanners, it is become essential to automate the conventional image-processing techniques to assist pathologists with some tasks such as mitotic-cells detection. In histopathological images analysing, the mitotic-cells counting is a significant biomarker in the prognosis of the breast cancer grade and its aggressiveness. However, counting task of mitotic-cells is tiresome, tedious and time-consuming due to difficulty distinguishing between mitotic cells and normal cells. To tackle this challenge, several deep learning-based approaches of Computer-Aided Diagnosis (CAD) have been lately advanced to perform counting task of mitotic-cells in the histopathological images. Such CAD systems achieve outstanding performance, hence histopathologists can utilise them as a second-opinion system. However, improvement of CAD systems is an important with the progress of deep learning networks architectures. In this work, we investigate deep YOLO (You Only Look Once) v2 network for mitotic-cells detection on ICPR (International Conference on Pattern Recognition) 2012 dataset of breast cancer histopathology. The obtained results showed that proposed architecture achieves good result of 0.839 F1-measure.

Since February 2020, the world is in a COVID-19 pandemic [1]. Researchers around the globe are pitching in to develop a fast reliable, non-invasive testing methodology to solve this problem and one of the key directions of research is to utilize coughs and their corresponding vocal biomarkers for diagnosis of COVID-19. In this thesis, we propose a fast, real-time cough detection pipeline that can be used to detect and localize coughs from audio samples. The core of the pipeline utilizes the yolo-v3 model [2] from vision domain to localize coughs in the audio spectrograms by treating them as objects. This outcome is transformed to localize the boundaries of cough utterances in the input signal. The system to detect coughs from CoughVid dataset [3] is then evaluated. Furthermore, the pipeline is compared with other existing algorithms like tinyyolo-v3 to test for better localization and classification. Average precision(AP@0.5) of yolo-v3 and tinyyolo-v3 model are 0.67 and 0.78 respectively. Based on the AP values, tinyyolo-v3 performs better than yolo-v3 by atleast 10% and based on its computational advantage, its inference time was also found to be 2.4 times faster than yolo-v3 model in our experiments. This work is considered to be novel and significant in detection and localization of cough in an audio stream. In the end, the resulting cough events are used to extract MFCC features from it and classifiers were trained to predict whether a cough has COVID-19 or not. The performance of different classifiers were compared and it was observed that random forest outperformed other models with a precision of 83.04%. It can also be inferred from the results that the classifier looks promising, however, in future this model has to be trained using clinically approved dataset and tested for its reliability in using this model in a clinical setup. / Sedan februari 2020 är världen inne i en COVID-19-pandemi [1]. Forskare runt om i världen satsar på att utveckla en snabb tillförlitlig, icke-invasiv testmetodik för att lösa detta problem och en av de viktigaste forskningsriktningarna är att använda hosta och deras motsvarande vokala biomarkörer för diagnos av COVID-19. I denna avhandling föreslår vi en snabb pipeline för hostdetektering i realtid som kan användas för att upptäcka och lokalisera hosta från ljudprover. Kärnan i rörledningen använder yolo-v3-modellen [2] från syndomänen för att lokalisera hosta i ljudspektrogrammen genom att behandla dem som objekt. Detta resultat transformeras för att lokalisera gränserna för hosta yttranden i insignalen. Systemet för att upptäcka hosta från CoughVid dataset [3] utvärderas sedan. Dessutom jämförs rörledningen med andra befintliga algoritmer som tinyyolo-v3 för att testa för bättre lokalisering och klassificering. Genomsnittlig precision (AP@0.5) för modellen yolo-v3 och tinyyolo-v3 är 0,67 respektive 0,78. Baserat på AP-värdena fungerar tinyyolo-v3 bättre än yolo-v3 med minst 10% och baserat på dess beräkningsfördel befanns dess inferenstid också vara 2,4 gånger snabbare än yolo-v3- modellen i våra experiment. Detta arbete anses vara nytt och viktigt för att upptäcka och lokalisera hosta i en ljudström. I slutändan används de resulterande hosthändel-serna för att extrahera MFCC-funktioner från det och klassificerare utbildades för att förutsäga om en hosta har COVID-19 eller inte. Prestanda för olika klassificerare jämfördes och det observerades att slumpmässig skog överträffade andra modeller med en precision på 83.04%. Av resultaten kan man också dra slutsatsen att klassificeraren ser lovande ut, men i framtiden måste denna modell utbildas med hjälp av kliniskt godkänd dataset och testas med avseende på dess tillförlitlighet vid användning av denna modell i ett kliniskt upplägg.

Självkörande fordon kan minska trafikstockningar och minska antalet trafikrelaterade olyckor. Då det i framtiden kommer att finnas miljontals autonoma fordon krävs en bättre förståelse av omgivningen. Syftet med detta projekt är att skapa ett externt automatiskt trafikledningssystem som kan upptäcka och spåra 3D-objekt i en komplex trafiksituation för att senare skicka beteendet från dessa objekt till ett större projekt som hanterar med att 3D-modellera trafiksituationen. Projektet använder sig av Tensorflow ramverket och YOLOv3 algoritmen. Projektet använder sig även av en kamera för att spela in trafiksituationer och en dator med Linux som operativsystem. Med hjälp av metoder som vanligen används för att skapa ett automatiserat trafikledningssystem utvärderades ett målföljningssystem. De slutliga resultaten visar att systemet är relativt instabilt och ibland inte kan känna igen vissa objekt. Om fler bilder används för träningsprocessen kan ett robustare och mycket mer tillförlitligt system utvecklas med liknande metodik. / Autonomous vehicles can decrease traffic congestion and reduce the amount of traffic related accidents. As there will be millions of autonomous vehicles in the future, a better understanding of the environment will be required. This project aims to create an external automated traffic system that can detect and track 3D objects within a complex traffic situation to later send these objects’ behavior for a larger-scale project that manages to 3D model the traffic situation. The project utilizes Tensorflow framework and YOLOv3 algorithm. The project also utilizes a camera to record traffic situations and a Linux operated computer. Using methods commonly used to create an automated traffic management system was evaluated. The final results show that the system is relatively unstable and can sometimes fail to recognize certain objects. If more images are used for the training process, a more robust and much more reliable system could be developed using a similar methodology.

Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more conventional with different types of Convolutional Neural Networks. A segmentation network was used as well as a few detection networks and different fusions between these. The Convolutional Neural Network approach proved to be the better of the two in terms of precision and recall but the clustering approach was not far behind. This work was done using a relatively small amount of data which potentially could have impacted the results of the Machine Learning models in a negative way.