Active Learning for Deep Object Detection

Brust, Clemens-Alexander; Käding, Christoph; Denzler, Joachim

doi:10.5220/0007248601810190

Cited by 52 publications

(41 citation statements)

References 31 publications

(51 reference statements)

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“…The first works that handled active learning for large scale object detection [26] used as active learning criterion the simple margin selection method for SVMs [27], which seeks points that most reduce the version space. More recently, methods rely on modern object detectors [28] [29], but still are based on uncertainty indicators like least confidence or 1-vs-2 [30] [31]. Notice that object detection is very close to the instance segmentation task addressed in this work.…”

Section: Related Workmentioning

confidence: 98%

Mask-guided sample selection for semi-supervised instance segmentation

Bellver

Salvador

Torres

et al. 2020

Multimed Tools Appl

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Image segmentation methods are usually trained with pixel-level annotations, which require significant human effort to collect. The most common solution to address this constraint is to implement weakly-supervised pipelines trained with lower forms of supervision, such as bounding boxes or scribbles. Another option are semi-supervised methods, which leverage a large amount of unlabeled data and a limited number of strongly-labeled samples. In this second setup, samples to be strongly-annotated can be selected randomly or with an active learning mechanism that chooses the ones that will maximize the model performance. In this work, we propose a sample selection approach to decide which samples to annotate for semi-supervised instance segmentation. Our method consists in first predicting pseudo-masks for the unlabeled pool of samples, together with a score predicting the quality of the mask. This score is an estimate of the Intersection Over Union (IoU) of the segment with the ground truth mask. We study which samples are better to annotate given the quality score, and show how our approach outperforms a random selection, leading to improved performance for semi-supervised instance segmentation with low annotation budgets.

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Section: Related Workmentioning

confidence: 98%

Mask-guided sample selection for semi-supervised instance segmentation

Bellver

Salvador

Torres

et al. 2020

Multimed Tools Appl

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“…For each image, margin is chosen to be the summation of margins across all the predicted bounding boxes in the image. This is taken from Brust et al [18]. • Entropy (ent): Samples with high entropy in the probability distribution of the predictions are selected.…”

Section: Comparison With Baselinesmentioning

confidence: 99%

Active learning with point supervision for cost-effective panicle detection in cereal crops

et al. 2020

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Background: Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquisition of huge amount of labeled image datasets for each crop is expensive and time-consuming. Thus, to catalyze the widespread usage of automatic object detection for crop phenotyping, a cost-effective method to develop such automated systems is essential.Results: We propose a weak supervision based active learning approach for panicle detection in cereal crops. In our approach, the model constantly interacts with a human annotator by iteratively querying the labels for only the most informative images, as opposed to all images in a dataset. Our query method is specifically designed for cereal crops which usually tend to have panicles with low variance in appearance. Our method reduces labeling costs by intelligently leveraging low-cost weak labels (object centers) for picking the most informative images for which strong labels (bounding boxes) are required. We show promising results on two publicly available cereal crop datasets -Sorghum and Wheat. On Sorghum, 6 variants of our proposed method outperform the best baseline method with more than 55% savings in labeling time. Similarly, on Wheat, 3 variants of our proposed methods outperform the best baseline method with more than 50% of savings in labeling time. Conclusion:We proposed a cost effective method to train reliable panicle detectors for cereal crops. A low cost panicle detection method for cereal crops is highly beneficial to both breeders and agronomists. Plant breeders can obtain quick crop yield estimates to make important crop management decisions. Similarly, obtaining real time visual crop analysis is valuable for researchers to analyze the crop's response to various experimental conditions.

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“…Many computer vision studies are also underway to minimize the amount of training data and human effort while maximizing the model performance. For example, Roy et al (2018) and Brust et al (2019) employed active learning algorithms to train an object detector. As active learning algorithms select the most informative-to-learn instances from abundant unlabeled training data and train a deep learning model with the selected data first, it is possible to significantly reduce the amount of training data and the human effort required for DB development (Kim et al, 2020b).…”

Section: Database-free Vision-based Monitoringmentioning

confidence: 99%

Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions

Kim

2020

Front. Built Environ.

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Operation-level vision-based monitoring and documentation has drawn significant attention from construction practitioners and researchers. To automate the operation-level monitoring of construction and built environments, there have been much effort to develop computer vision technologies. Despite their encouraging findings, it remains a major challenge to exploit technologies in real construction projects, implying that there are knowledge gaps in practice and theory. To fill such knowledge gaps, this study thoroughly reviews 119 papers on operation-level vision-based construction monitoring, published in mainstream construction informatics journals. Existing research papers can be categorized into three sequential technologies: (1) camera placement for operation-level construction monitoring, (2) single-camera-based construction monitoring and documentation, and (3) multi-camera-based onsite information integration and construction monitoring. For each technology, state-of-the-art algorithms, open challenges, and future directions are discussed.

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Active Learning for Deep Object Detection

Cited by 52 publications

References 31 publications

Mask-guided sample selection for semi-supervised instance segmentation

Mask-guided sample selection for semi-supervised instance segmentation

Active learning with point supervision for cost-effective panicle detection in cereal crops

Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions

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