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• W4210368916 endingPage "14119" @default.
• W4210368916 startingPage "14076" @default.
• W4210368916 abstract "Nowadays Convolutional Neural Networks (CNNs) are being employed in a wide range of industrial technologies for a variety of sectors, such as medical, automotive, aviation, agriculture, space, etc. This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way. Layer-based details of CNNs along with parameter and floating-point operation number calculations are outlined. Using an evolutionary approach, the majority of the outstanding image classification CNNs, published in the open literature, is introduced with a focus on their accuracy performance, parameter number, model size, and inference speed, highlighting the progressive developments in convolutional operations. Results of a novel investigation of the convolution types and operations commonly used in CNNs are presented, including a timing analysis aimed at assessing their impact on CNN performance. This extensive experimental study provides new insight into the behaviour of each convolution type in terms of training time, inference time, and layer level decomposition. Building blocks for CNN-based object detection are also discussed, such as backbone networks and baseline types, and then representative state-of-the-art designs are outlined. Experimental results from the literature are summarised for each of the reviewed models. This is followed by an overview of recent ADSs related works and current industry activities, aiming to bridge academic research and industry practice on CNNs and ADSs. Design approaches targeted at solving problems of automakers in achieving real-time implementations are also proposed based on a discussion of design constraints, human vs. machine evaluations and trade-off analysis of accuracy vs. size. Current technologies, promising directions, and expectations from the literature on ADSs are introduced including a comprehensive trade-off analysis from a human-machine perspective." @default.
• W4210368916 created "2022-02-08" @default.
• W4210368916 creator A5012671898 @default.
• W4210368916 creator A5062842165 @default.
• W4210368916 date "2022-01-01" @default.
• W4210368916 modified "2023-10-05" @default.
• W4210368916 title "Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey" @default.
• W4210368916 cites W1522734439 @default.
• W4210368916 cites W1527516633 @default.
• W4210368916 cites W1536680647 @default.
• W4210368916 cites W1576357919 @default.
• W4210368916 cites W1763528081 @default.
• W4210368916 cites W1849277567 @default.
• W4210368916 cites W1861492603 @default.
• W4210368916 cites W1969483458 @default.
• W4210368916 cites W1969640700 @default.
• W4210368916 cites W1969732712 @default.
• W4210368916 cites W1970789124 @default.
• W4210368916 cites W1978301848 @default.
• W4210368916 cites W1988790447 @default.
• W4210368916 cites W1991083751 @default.
• W4210368916 cites W1994616650 @default.
• W4210368916 cites W1994924587 @default.
• W4210368916 cites W1996901117 @default.
• W4210368916 cites W2002517365 @default.
• W4210368916 cites W2005136695 @default.
• W4210368916 cites W2009345891 @default.
• W4210368916 cites W2011301426 @default.
• W4210368916 cites W2020062325 @default.
• W4210368916 cites W2021063678 @default.
• W4210368916 cites W2024060531 @default.
• W4210368916 cites W2030507413 @default.
• W4210368916 cites W2031489346 @default.
• W4210368916 cites W2032924574 @default.
• W4210368916 cites W2038794597 @default.
• W4210368916 cites W2039708501 @default.
• W4210368916 cites W2052742500 @default.
• W4210368916 cites W2067824962 @default.
• W4210368916 cites W2071039340 @default.
• W4210368916 cites W2074437835 @default.
• W4210368916 cites W2075919249 @default.
• W4210368916 cites W2078584927 @default.
• W4210368916 cites W2086807722 @default.
• W4210368916 cites W2088049833 @default.
• W4210368916 cites W2091845343 @default.
• W4210368916 cites W2097117768 @default.
• W4210368916 cites W2098100495 @default.
• W4210368916 cites W2098476269 @default.
• W4210368916 cites W2098519927 @default.
• W4210368916 cites W2100193157 @default.
• W4210368916 cites W2101926813 @default.
• W4210368916 cites W2102605133 @default.
• W4210368916 cites W2102664428 @default.
• W4210368916 cites W2105100091 @default.
• W4210368916 cites W2107656445 @default.
• W4210368916 cites W2109255472 @default.
• W4210368916 cites W2109797278 @default.
• W4210368916 cites W2112796928 @default.
• W4210368916 cites W2113723575 @default.
• W4210368916 cites W2116165149 @default.
• W4210368916 cites W2116360511 @default.
• W4210368916 cites W2117539524 @default.
• W4210368916 cites W2119112357 @default.
• W4210368916 cites W2121900453 @default.
• W4210368916 cites W2127578024 @default.
• W4210368916 cites W2129577383 @default.
• W4210368916 cites W2132083787 @default.
• W4210368916 cites W2136922672 @default.
• W4210368916 cites W2137401668 @default.
• W4210368916 cites W2139137304 @default.
• W4210368916 cites W2143083884 @default.
• W4210368916 cites W2144506857 @default.
• W4210368916 cites W2148361676 @default.
• W4210368916 cites W2150066425 @default.
• W4210368916 cites W2150418461 @default.
• W4210368916 cites W2151103935 @default.
• W4210368916 cites W2155893237 @default.
• W4210368916 cites W2159132531 @default.
• W4210368916 cites W2160960847 @default.
• W4210368916 cites W2161969291 @default.
• W4210368916 cites W2163922914 @default.
• W4210368916 cites W2164595491 @default.
• W4210368916 cites W2165698076 @default.
• W4210368916 cites W2168356304 @default.
• W4210368916 cites W2170003873 @default.
• W4210368916 cites W2174983451 @default.
• W4210368916 cites W2183341477 @default.
• W4210368916 cites W2194775991 @default.
• W4210368916 cites W2206222117 @default.
• W4210368916 cites W2233116163 @default.
• W4210368916 cites W2270147915 @default.
• W4210368916 cites W2285660444 @default.
• W4210368916 cites W2294370754 @default.
• W4210368916 cites W2296452361 @default.
• W4210368916 cites W2316564661 @default.
• W4210368916 cites W2340897893 @default.
• W4210368916 cites W2342249984 @default.
• W4210368916 cites W2395611524 @default.

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