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4àjIq'~ ɯo:Ukzbg}IxK"xv( ws"Vwm2X&pdfC>ulp!x)KW'sY(C8ʐ7Y(öulAiBF( (qAQpT*?D %baa0h49R2sAֳ#A魛aArPrg!e'z2h[PfdDw%`~Ac`TI[O2ˬx7U./[].?7֡^Y$N%=7> ۋ~rFkyMf?\UTkie#+E Ҝ/4.ܼ@up#ǭDM/{Tl[Pb6T2{I޷S75J}y;Qhl+sM-O>^~$ČGKRlc# @'1x%h$Z5w˼th)O G ]}Fo- 7MVE۴/4zn/l].O4Q.g;?k|g2 )Ħ^}^ͪ'5Zc^QuK44~'P$ a8svſ qi*+c:1JyzGYBH{^E>M! s6/ |NoZ~k=zz8bu||.G!FL*TpKԬe%^uRo 6AUkH8Ueac'OQJF6:&N[quT=.j]5X:)VѺ +r:^׮YOֵڧRcP];ZV+!Bu}YiJPzdy ,Ę˂̄cjIP];t*CZj \ S^ߥ!q1ZQ76|`( Sd6Q壝jhѨM.t\jF3F8ZΙ{bs6a`(=tθҝSQU: GRSo; 'IҁF(-HAZNu 4!mr- )l0,;2 &-(yDJc-2:++,:Tx?<,m5ɵMtmh}:7Qz0)jS^oGcogvVYogvVO|V{>>9ijr՞6ˑ<Ғ\4/wi0q&L"iטBw=߿ r@"h r OI㣵&?sFA'PlLj*KJfƒ4)[.'MbiH9:JCdlHR2:R>=~Ehq€(W&ȶ$ endstream endobj 14 0 obj << /Type /StructElem /S /Part /P 8 0 R /Pg 7 0 R /K [15 0 R 16 0 R 17 0 R 114 0 R 18 0 R 115 0 R 19 0 R 116 0 R 20 0 R 21 0 R 22 0 R 123 0 R 43 0 R 44 0 R 45 0 R 46 0 R 124 0 R 62 0 R 125 0 R 67 0 R 126 0 R 83 0 R 127 0 R 128 0 R 110 0 R 129 0 R] >> endobj 15 0 obj << /Type /StructElem /S /H1 /T (Understanding the Role of AI-Based Object Detection Models in Autonomous Vehicles) /E (Understanding the Role of AI-Based Object Detection Models in Autonomous Vehicles) /Pg 7 0 R /K [71] >> endobj 16 0 obj << /Type /StructElem /S /Figure /P 14 0 R /Pg 7 0 R /K [96] >> endobj 17 0 obj << /Type /StructElem /S /H2 /T <4E617468616E2046657272656E6365207C20436F6D707574657220536369656E6365207C20416476697365642062792044722E20417361906420417361906420> /E <4E617468616E2046657272656E6365207C20436F6D707574657220536369656E6365207C20416476697365642062792044722E20417361906420417361906420> /Pg 7 0 R /K [70] >> endobj 18 0 obj << /Type /StructElem /S /H3 /T (Methodology) /E (Methodology) /Pg 7 0 R /K [74] >> endobj 19 0 obj << /Type /StructElem /S /H3 /T (Overview) /E (Overview) /Pg 7 0 R /K [72] >> endobj 20 0 obj << /Type /StructElem /S /H3 /T (Results) /E (Results) /Pg 7 0 R /K [73] >> endobj 21 0 obj << /Type /StructElem /S /Figure /P 14 0 R /Pg 7 0 R /K [100] >> endobj 22 0 obj << /Type /StructElem /S /Figure /P 14 0 R /Pg 7 0 R /K [101] >> endobj 23 0 obj << /Type /StructElem /S /P /P 130 0 R /E (Model) /Pg 7 0 R /K [0] >> endobj 24 0 obj << /Type /StructElem /S /P /P 131 0 R /E (Baseline mAP%) /Pg 7 0 R /K [1] >> endobj 25 0 obj << /Type /StructElem /S /P /P 132 0 R /E (New mAP%) /Pg 7 0 R /K [2] >> endobj 26 0 obj << /Type /StructElem /S /P /P 133 0 R /E (Difference) /Pg 7 0 R /K [3] >> endobj 27 0 obj << /Type /StructElem /S /P /P 134 0 R /E (Faster-RCNN) /Pg 7 0 R /K [4] >> endobj 28 0 obj << /Type /StructElem /S /P /P 135 0 R /E (73.3%) /Pg 7 0 R /K [5] >> endobj 29 0 obj << /Type /StructElem /S /P /P 136 0 R /E (42.78%) /Pg 7 0 R /K [6] >> endobj 30 0 obj << /Type /StructElem /S /P /P 137 0 R /E (-30.52%) /Pg 7 0 R /K [7] >> endobj 31 0 obj << /Type /StructElem /S /P /P 138 0 R /E (YOLO) /Pg 7 0 R /K [8] >> endobj 32 0 obj << /Type /StructElem /S /P /P 139 0 R /E (80.7%) /Pg 7 0 R /K [9] >> endobj 33 0 obj << /Type /StructElem /S /P /P 140 0 R /E (82.25%) /Pg 7 0 R /K [10] >> endobj 34 0 obj << /Type /StructElem /S /P /P 141 0 R /E (+1.55%) /Pg 7 0 R /K [11] >> endobj 35 0 obj << /Type /StructElem /S /P /P 142 0 R /E (RetinaNet) /Pg 7 0 R /K [12] >> endobj 36 0 obj << /Type /StructElem /S /P /P 143 0 R /E (13.74%) /Pg 7 0 R /K [13] >> endobj 37 0 obj << /Type /StructElem /S /P /P 144 0 R /E (14.48%) /Pg 7 0 R /K [14] >> endobj 38 0 obj << /Type /StructElem /S /P /P 145 0 R /E (+0.74%) /Pg 7 0 R /K [15] >> endobj 39 0 obj << /Type /StructElem /S /P /P 146 0 R /E (SSD) /Pg 7 0 R /K [16] >> endobj 40 0 obj << /Type /StructElem /S /P /P 147 0 R /E (68.06%) /Pg 7 0 R /K [17] >> endobj 41 0 obj << /Type /StructElem /S /P /P 148 0 R /E (78.08%) /Pg 7 0 R /K [18] >> endobj 42 0 obj << /Type /StructElem /S /P /P 149 0 R /E (+10.02%) /Pg 7 0 R /K [19] >> endobj 43 0 obj << /Type /StructElem /S /P /E (Autonomous navigation is a critical component of autonomous vehicles. This requires precise object detection and real-time decisions. This project explores four state-of-the-art object detection algorithms: Faster-RCNN, YOLO, RetinaNet, and SSD. Models were trained on their respective datasets. Implemented using TensorFlow, PyTorch, and OpenCV, assessing their mean average precision score. The results offer insights into optimizing object detection by applying structural changes and data augmentation techniques. This study contributes to the development of efficient AI-driven perception systems for self driving applications.) /Pg 7 0 R /K [77] >> endobj 44 0 obj << /Type /StructElem /S /P /E (Algorithms will be evaluated and then optimization techniques will be applied to improve algorithm mean average precision scores \(mAP\). The algorithms will be tested on their respective datasets, comparing baseline mAP scores and optimized mAP scores. 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