{"id":33,"date":"2022-12-15T05:12:49","date_gmt":"2022-12-15T05:12:49","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2023team3\/?page_id=33"},"modified":"2023-05-09T03:38:18","modified_gmt":"2023-05-09T03:38:18","slug":"results","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2023team3\/results\/","title":{"rendered":"Results"},"content":{"rendered":"\n

Weather Classification <\/h2>\n\n\n\n

After aggregating all of the datasets together and computing features for all of them, we achieved 87% accuracy for weather type classification and 93% accuracy for time of day classification. Many of the inaccuracies for time of day come from distinguishing dawn\/dusk from day or night. Finally, road type classification is currently still in progress and takes more time due to manual labeling and segmentation latencies. <\/p>\n\n\n\n

Image Restoration<\/h2>\n\n\n\n

The image restoration model was evaluated on real-world images. Below are the qualitative results on DAWN dataset, Dashcam videos and BDD100K test dataset.<\/p>\n\n\n

\n
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Input foggy image(left) and output restored image(right) on DAWN dataset<\/figcaption><\/figure>\n<\/div>\n\n
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Input foggy image(left) and output restored image(right) on dashcam videos<\/figcaption><\/figure>\n<\/div>\n\n
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Input foggy image(left) and output restored image(right) on BDD100K dataset night-time test images<\/figcaption><\/figure>\n<\/div>\n\n\n
mAP pre-restoration<\/strong><\/td>mAP post-restoration<\/strong><\/td><\/tr>
26.01<\/td>31.33<\/td><\/tr><\/tbody><\/table>
Object detection results<\/figcaption><\/figure>\n\n\n\n

For evaluation, we performed object detection on images before and after restoration. The obtained mAP values in the above table shows improvement in object detection post-restoration. Below are some example cases where the detection fails in images before restoration but is successful after restoration. <\/p>\n\n\n\n

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Evaluation on object detection<\/figcaption><\/figure>\n\n\n\n

<\/h3>\n","protected":false},"excerpt":{"rendered":"

Weather Classification After aggregating all of the datasets together and computing features for all of them, we achieved 87% accuracy for weather type classification and 93% accuracy for time of day classification. Many of the inaccuracies for time of day come from distinguishing dawn\/dusk from day or night. Finally, road type classification is currently still … <\/p>\n

Continue reading “Results”<\/span><\/a><\/p>\n","protected":false},"author":148,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-33","page","type-page","status-publish","hentry"],"yoast_head":"\nResults - Autonomous Driving in Adverse Weather Conditions<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2023team3\/results\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Results - Autonomous Driving in Adverse Weather Conditions\" \/>\n<meta property=\"og:description\" content=\"Weather Classification After aggregating all of the datasets together and computing features for all of them, we achieved 87% accuracy for weather type classification and 93% accuracy for time of day classification. 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