Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2308.07106 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917778788712448 |
|---|---|
| author | Hoss, Michael |
| author_facet | Hoss, Michael |
| contents | The object perception of automated driving systems must pass quality and robustness tests before a safe deployment. Such tests typically identify true positive (TP), false-positive (FP), and false-negative (FN) detections and aggregate them to metrics. Since the literature seems to be lacking a comprehensive way to define the identification of TPs/FPs/FNs, this paper provides a checklist of relevant functional aspects and implementation details. Besides labeling policies of the test set, we cover areas of vision, occlusion handling, safety-relevant areas, matching criteria, temporal and probabilistic issues, and further aspects. Even though the checklist cannot be fully formalized, it can help practitioners minimize the ambiguity of their tests, which, in turn, makes statements on object perception more reliable and comparable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_07106 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Checklist to Define the Identification of TP, FP, and FN Object Detections in Automated Driving Hoss, Michael Computer Vision and Pattern Recognition Robotics The object perception of automated driving systems must pass quality and robustness tests before a safe deployment. Such tests typically identify true positive (TP), false-positive (FP), and false-negative (FN) detections and aggregate them to metrics. Since the literature seems to be lacking a comprehensive way to define the identification of TPs/FPs/FNs, this paper provides a checklist of relevant functional aspects and implementation details. Besides labeling policies of the test set, we cover areas of vision, occlusion handling, safety-relevant areas, matching criteria, temporal and probabilistic issues, and further aspects. Even though the checklist cannot be fully formalized, it can help practitioners minimize the ambiguity of their tests, which, in turn, makes statements on object perception more reliable and comparable. |
| title | Checklist to Define the Identification of TP, FP, and FN Object Detections in Automated Driving |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2308.07106 |