Continuous learning with random memory for object detection in robotic applications

Nenakhov, Ivan; Mazhitov, Ruslan; Artemov, Kirill; Zabihifar, Seyedhassan; Semochkin, Aleksandr; Kolyubin, Sergey A.

doi:10.1109/nir52917.2021.9666113

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Such assignment is a challenge that resides at the core of all the aforementioned dynamic learning paradigms. In particular, the focus here is on case study examples where new exceptions and categories are learned in real time so that mitigation of the phenomenon that has been identified as 'catastrophic forgetting' [10,[13][14][15][16][17][18] is considered.…”

Section: Literature Studymentioning

confidence: 99%

A Survey of Incremental Deep Learning for Defect Detection in Manufacturing

Mohandas,

Southern,

O’Connell

et al. 2024

BDCC

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Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedures to dynamically update model-based detection methods that use sequential streaming during the training phase. This paper reviews how new process, training or validation information is rigorously incorporated in real time when detection exceptions arise during inspection. In particular, consideration is given to how new tasks, classes or decision pathways are added to existing models or datasets in a controlled fashion. An analysis of studies from the incremental learning literature is presented, where the emphasis is on the mitigation of process complexity challenges such as, catastrophic forgetting. Further, practical implementation issues that are known to affect the complexity of deep learning model architecture, including memory allocation for incoming sequential data or incremental learning accuracy, is considered. The paper highlights case study results and methods that have been used to successfully mitigate such real-time manufacturing challenges.

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Section: Literature Studymentioning

confidence: 99%

A Survey of Incremental Deep Learning for Defect Detection in Manufacturing

Mohandas,

Southern,

O’Connell

et al. 2024

BDCC

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“…Sequential learning in object detection refers to the ability to continually learn new tasks without forgetting previously learned knowledge. In related literature, several approaches addressing similar methods as sequential learning have been explored, such as incremental learning [19][20][21], continuous learning [22][23][24], and application transfer learning [25,26]. Nenakhov et al [19] incrementally introduce new object classes to the model using a method referred to as random memory.…”

Section: Related Workmentioning

confidence: 99%

“…In related literature, several approaches addressing similar methods as sequential learning have been explored, such as incremental learning [19][20][21], continuous learning [22][23][24], and application transfer learning [25,26]. Nenakhov et al [19] incrementally introduce new object classes to the model using a method referred to as random memory. These object classes are from the CORe50 dataset and include items such as scissors, plug adapters, mobile phones, and light bulbs.…”

Section: Related Workmentioning

confidence: 99%

Sequential Learning of Flame Objects Sorted by Size for Early Fire Detection in Surveillance Videos

Samosir,

Nguyen,

Kong

2024

Electronics

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This paper presents a sequential learning method aimed at improving the performance of a lightweight deep learning model used for detecting fires at an early stage in surveillance video streams. The proposed approach involves a sequence of supervised learning steps, wherein the entire training dataset is partitioned into multiple sub-datasets based on the size of fire objects. The size of fire objects is measured by object size ratio, which is the ratio of the bounding box area of the detected fire flame object relative to the entire image area. The initial training sub-dataset contains the largest-sized fire objects, progressing to the final sub-dataset containing the smallest-sized fire objects. The objective is to employ sequential learning to enhance the detection of small-sized fire objects relative to the image area using a lightweight model suitable for edge computing devices. Experiment results demonstrate that a deep learning fire detection model trained sequentially with a descending order of object size can effectively detect small flame objects with an object size ratio less than 0.006, achieving an F1 score of 93.1%, representing a 27% improvement compared to traditional supervised learning with no sequential learning steps. Additionally, performance in detecting tiny flame objects with an object size ratio less than 0.0016 achieves an F1 score of 94.5%, showing a 17.5% increase compared to the baseline without sequential learning.

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Continuous learning with random memory for object detection in robotic applications

Cited by 2 publications

References 17 publications

A Survey of Incremental Deep Learning for Defect Detection in Manufacturing

A Survey of Incremental Deep Learning for Defect Detection in Manufacturing

Sequential Learning of Flame Objects Sorted by Size for Early Fire Detection in Surveillance Videos

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