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This research explores the capacity for functional photoreception in larvae of the mountain pine beetle (Dendroctonus ponderosae), an extremely important forest pest insect that is well adapted for development beneath the bark of pine trees. Phototaxis tests, gene expression analysis and development experiments were integrated to assess mountain pine beetle larvae for light sensitivity. When presented with a phototaxis choice test, larvae preferred dark over light microhabitats, revealing that larvae sense and respond behaviourally to light. Long wavelength opsin transcription was identified in all life stages, including eggs and larvae, suggesting that D. ponderosae possesses extraretinal photosensitive capabilities across its life cycle. The long wavelength opsin could function in phototaxis or the development phenology of immature beetles, while the ultraviolet opsin, only found to be expressed in pupae and adults, is likely to function in dispersal via the compound eyes. Results from two development experiments reveal an effect of photoperiod treatment on beetle development rate when reared from the egg stage, but not when reared from mature larvae, indicating that a critical photosensitive life stage(s) must occur in D. ponderosae prior to the third larval instar. An effect of photoperiod on adult emergence rates, however, appears to be independent of larval rearing conditions. The discovery of opsin expression and negative phototaxis in eyeless mountain pine beetle larvae, in addition to an effect of photoperiod on immature development and adult emergence rates, suggest that light and photoperiodism likely function in survival and life cycle coordination in this species. / Graduate / 2018-10-17

Evacuation of trains in tunnels is currently taking place in diverse ways. One of the methods implies that passengers leave the train along the railways on elevated walkways. The knowledge about the impact of elevated walkways on the safety level is today very limited. As more and more elevated walkways are designed in rail tunnels, it is important that studies and evacuation trials are made to investigate how those affect the safety level of the evacuation. This master thesis’ project aims to investigate the relationship between low light conditions, people's behaviour and ability to evacuate a train on an elevated walkway. To investigate this, the core in the work consisted a practical evacuation trial which purpose was to study people’s movement on a raised walkway under different illumination levels: 200 lux, 5 lux and 1 lux. A total of 16 escape trials were performed as controlled evacuations on a simulated elevated walkway with the measures 1.2x20 meters with a level difference down to the ground plane of 1.24 meters. The result of the evacuation trial showed that the intensity of illumination played a significant role in both the movement speed, the person flow and the peoples distance to the edge. The peoples flow and speed did generally decrease during the partial trials performed during the weaker light intensities, 5 lux and 1 lux, compared to partial trials performed during 200 lux. A general result regarding the effect of light intensity on the people’s distance to the edge is that during the partial trials performed with the weaker light intensities, 5 lux and 1 lux, more people chose to go further from the edge. The conclusion of these results is that a minimum brightness in tunnels should be 1 lux, but a stronger illumination should be sought to increase the safety of the passengers in case of evacuation. Regarding the learning effect on the trial procedure, it can be seen from the results that the more trials carried out, the closer the edge the people went combined with an increased speed and flow. The people became comfortable in the environment and hesitated less, which generated a source of error in the result. A conclusion of the practical evacuation trial is that by conducting a trial in this type of environment, we were given the opportunity to study the problems as well as the complexity that an evacuation may imply. The experiment further provided valuable information and knowledge about the problems that may arise in an evacuation, both from a technical and behavioural perspective.

Computer vision is a key component of any autonomous system. Real world computer vision applications rely on a proper and accurate detection and classification of objects. A detection algorithm that doesn’t guarantee reasonable detection accuracy is not applicable in real time scenarios where safety is the main objective. Factors that impact detection accuracy are illumination conditions and image resolution. Both contribute to degradation of objects and lead to low classifications and detection accuracy. Recent development of Convolutional Neural Networks (CNNs) based algorithms offers possibilities for low-light (LL) image enhancement and super resolution (SR) image generation which makes it possible to combine such models in order to improve image quality and increase detection accuracy. This thesis evaluates different CNNs models for SR generation and LL enhancement by comparing generated images against ground truth images. To quantify the impact of the respective model on detection accuracy, a detection procedure was evaluated on generated images. Experimental results evaluated on images selected from NoghtOwls and Caltech Pedestrian datasets proved that super resolution image generation and low-light image enhancement improve detection accuracy by a substantial margin. Additionally, it has been proven that a cascade of SR generation and LL enhancement further boosts detection accuracy. However, the main drawback of such cascades is related to an increased computational time which limits possibilities for a range of real time applications. / Datorseende är en nyckelkomponent i alla autonoma system. Applikationer för datorseende i realtid är beroende av en korrekt detektering och klassificering av objekt. En detekteringsalgoritm som inte kan garantera rimlig noggrannhet är inte tillämpningsbar i realtidsscenarier, där huvudmålet är säkerhet. Faktorer som påverkar detekteringsnoggrannheten är belysningförhållanden och bildupplösning. Dessa bidrar till degradering av objekt och leder till låg klassificerings- och detekteringsnoggrannhet. Senaste utvecklingar av Convolutional Neural Networks (CNNs) -baserade algoritmer erbjuder möjligheter för förbättring av bilder med dålig belysning och bildgenerering med superupplösning vilket gör det möjligt att kombinera sådana modeller för att förbättra bildkvaliteten och öka detekteringsnoggrannheten. I denna uppsats utvärderas olika CNN-modeller för superupplösning och förbättring av bilder med dålig belysning genom att jämföra genererade bilder med det faktiska data. För att kvantifiera inverkan av respektive modell på detektionsnoggrannhet utvärderades en detekteringsprocedur på genererade bilder. Experimentella resultat utvärderades på bilder utvalda från NoghtOwls och Caltech datauppsättningar för fotgängare och visade att bildgenerering med superupplösning och bildförbättring i svagt ljus förbättrar noggrannheten med en betydande marginal. Dessutom har det bevisats att en kaskad av superupplösning-generering och förbättring av bilder med dålig belysning ytterligare ökar noggrannheten. Den största nackdelen med sådana kaskader är relaterad till en ökad beräkningstid som begränsar möjligheterna för en rad realtidsapplikationer.

<p>Imaging and computer vision systems deployed in real-world environments face the challenge of accommodating a wide range of lighting conditions. However, the cost, the demand for high resolution, and the miniaturization of imaging devices impose physical constraints on sensor design, limiting both the dynamic range and effective aperture size of each pixel. Consequently, conventional CMOS sensors fail to deliver satisfactory capture in high dynamic range scenes or under photon-limited conditions, thereby impacting the performance of downstream vision tasks. In this thesis, we address two key problems: 1) exploring the utilization of spatial multiplexing, specifically spatially varying exposure tiling, to extend sensor dynamic range and optimize scene capture, and 2) developing techniques to enhance the robustness of object detection systems under photon-limited conditions.</p> <p><br></p> <p>In addition to challenges imposed by natural environments, real-world vision systems are susceptible to adversarial attacks in the form of artificially added digital content. Therefore, this thesis presents a comprehensive pipeline for constructing a robust and scalable system to counter such attacks.</p>