國立虎尾科技大學 |

Predicting YOLO Misdetection Using Feature Map Activations.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Predicting YOLO Misdetection Using Feature Map Activations./
作者:	Ferdous, Md. Rezoan.
面頁冊數:	1 online resource (59 pages)
附註:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798383058480

Predicting YOLO Misdetection Using Feature Map Activations.
Ferdous, Md. Rezoan.

Predicting YOLO Misdetection Using Feature Map Activations. - 1 online resource (59 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2024.

Includes bibliographical references

This research proposes a novel method to identify the YOLO object detection network failures. The proposed method employs a secondary neural network to predict misdetections (localization and classification error) based on the features extracted from the YOLO network. Moreover, to make the secondary network lightweight by selecting important features for a target class, a recursive feature elimination-based method is proposed. As a result, the computational cost is reduced without compromising the accuracy. Four of the most frequently occurring classes in the COCO dataset were taken into consideration when training the secondary network for the experimental evaluation. When a single class was taken into consideration, the proposed failure detection approach attained an accuracy of 89.79%; this is a 16% improvement in accuracy over the existing method. A 52% reduction in inference time and an accuracy of 88.90% were obtained with the feature selection approach. Additionally, the proposed failure detection framework was assessed by taking into account several classes at once, and excellent accuracy was noted.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383058480Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Detection network failuresIndex Terms--Genre/Form:

554714
Electronic books.

Predicting YOLO Misdetection Using Feature Map Activations.
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520 $a This research proposes a novel method to identify the YOLO object detection network failures. The proposed method employs a secondary neural network to predict misdetections (localization and classification error) based on the features extracted from the YOLO network. Moreover, to make the secondary network lightweight by selecting important features for a target class, a recursive feature elimination-based method is proposed. As a result, the computational cost is reduced without compromising the accuracy. Four of the most frequently occurring classes in the COCO dataset were taken into consideration when training the secondary network for the experimental evaluation. When a single class was taken into consideration, the proposed failure detection approach attained an accuracy of 89.79%; this is a 16% improvement in accuracy over the existing method. A 52% reduction in inference time and an accuracy of 88.90% were obtained with the feature selection approach. Additionally, the proposed failure detection framework was assessed by taking into account several classes at once, and excellent accuracy was noted.
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