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Evaluating rain removal image processing solutions for fast and accurate object detection
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Utvärdering av regnborttagningsalgoritmer för snabboch pålitlig objektigenkänning (Swedish)
Abstract [en]

Autonomous vehicles are an important topic in modern day research, both for the private and public sector. One of the reasons why self-driving cars have not yet reached consumer market is because of levels of uncertainty. This is often tackled with multiple sensors of different kinds which helps gaining robust- ness in the vehicle’s system. Radars, lidars and cameras are often the sensors used and the expenses can rise up quickly, which is not always feasible for different markets. This could be addressed with using fewer, but more robust sensors for visualization. This thesis addresses the issue of one particular failure mode for camera sensors, which is reduced view range affected by rainy weather. Kalman filter and discrete wavelet transform with bilateral filtering are evaluated as rain removal algorithms and tested with the state-of-the-art object detection algorithm, You Only Look Once (YOLOv3). Filtered videos in daylight and evening light were tested with YOLOv3 and results show that the accuracy is not improved enough to be worth implementing in autonomous vehicles. With the graphics card available for this thesis YOLOv3 is not fast enough for a vehicle to stop in time when driving in 110km/h and an obstacle appears 80m ahead, however an Nvidia Titan X is assumed to be fast enough. There is potential within the research area and this thesis suggests that other object detection methods are evaluated as future work.

Abstract [sv]

Autonoma fordon är för privat samt offentlig sektor ett viktigt område i modern forskning. Osäkerheten med autonoma fordon är en viktig anledning till varför de idag inte nått konsumentmarknaden. Systemen för autonoma fordon blir mer robusta med inkludering av flera sensorer av olika typer, vilka oftast är kameror, radar och lidars. Fordon med dessa sensorer kan snabbt öka i pris vilket gör dem mindre tillgängliga för olika marknader. Detta skulle kunna lösas med färre sensorer som däremot är mer robusta. Denna avhandling diskuterar problemet med en specific felmodell för kameror, vilket är minskat synfält som påverkas av regnigt väder. Kalman filter och diskret vågkomponent-transformation med bilateral filtrering utvärderades som regnborttagningsalgoritmer och testades med You Only Look Once (YOLOv3), en modern objektigenkänningsmetod. Filtrerade videofilmer i dagstid och kvällstid testades med YOLOv3 och resultaten visade att noggrannheten inte ökade tillräckligt mycket för att vara användbara för autonoma fordon. Med grafikkorten tillgängliga för denna avhandling är inte YOLOv3 snabb nog för ett fordon att hinna stanna i tid före kollision om bilen kör i 110km/h och ett föremål dyker upp 80m framför. Däremot antas det att fordon utrustade med Nvidias Titan X borde hinna stanna i tid före kollision. Avhandlingen ser däremot potential inom detta forskningsområde och föreslår att liknande test fast med andra objektigenkänningsmetoder bör utföras.

Place, publisher, year, edition, pages
2019.
Keywords [en]
object detection, failure modes, autonomous vehicles
Keywords [sv]
objektigenkänning, felmodell, autonoma fordon
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-254446OAI: oai:DiVA.org:kth-254446DiVA, id: diva2:1332740
Supervisors
Examiners
Available from: 2019-06-28 Created: 2019-06-28

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