Small Object Detection and Tracking: A Comprehensive Review

Mirzaei, Behzad; Nezamabadi‐pour, Hossein; Raoof, Amir; Derakhshani, Reza

doi:10.3390/s23156887

Cited by 27 publications

(9 citation statements)

References 93 publications

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“…The original Faster-RCNN object detection network only uses a single feature layer with high-order semantic information to predict target information. For small object detection, the high-order semantic feature maps lack the underlying information about details and have an impact on the accuracy of the detection results [ 25 , 26 ]. The new model leverages the multi-resolution feature maps from the feature extraction layer; these are merged through the feature fusion layer to retain the panoramic information on the targets.…”

Section: Methodsmentioning

confidence: 99%

Lightweight Detection Methods for Insulator Self-Explosion Defects

Chen,

Deng,

Sun

et al. 2024

Sensors

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The accurate and efficient detection of defective insulators is an essential prerequisite for ensuring the safety of the power grid in the new generation of intelligent electrical system inspections. Currently, traditional object detection algorithms for detecting defective insulators in images face issues such as excessive parameter size, low accuracy, and slow detection speed. To address the aforementioned issues, this article proposes an insulator defect detection model based on the lightweight Faster R-CNN (Faster Region-based Convolutional Network) model (Faster R-CNN-tiny). First, the Faster R-CNN model’s backbone network is turned into a lightweight version of it by substituting EfficientNet for ResNet (Residual Network), greatly decreasing the model parameters while increasing its detection accuracy. The second step is to employ a feature pyramid to build feature maps with various resolutions for feature fusion, which enables the detection of objects at various scales. In addition, replacing ordinary convolutions in the network model with more efficient depth-wise separable convolutions increases detection speed while slightly reducing network detection accuracy. Transfer learning is introduced, and a training method involving freezing and unfreezing the model is employed to enhance the network’s ability to detect small target defects. The proposed model is validated using the insulator self-exploding defect dataset. The experimental results show that Faster R-CNN-tiny significantly outperforms the Faster R-CNN (ResNet) model in terms of mean average precision (mAP), frames per second (FPS), and number of parameters.

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Section: Methodsmentioning

confidence: 99%

Lightweight Detection Methods for Insulator Self-Explosion Defects

Chen,

Deng,

Sun

et al. 2024

Sensors

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“…Mirzaei et al [23] contribute a comprehensive review on small object detection and tracking, addressing challenges and methods in computer vision. The study analyzes small object detection techniques, including methods based on CNNs, image processing algorithms, and machine learning techniques.…”

Section: You Only Look Once (Yolo)mentioning

confidence: 99%

Model Development for Identifying Aromatic Herbs Using Object Detection Algorithm

Antunes,

Okano,

Nääs

et al. 2024

AgriEngineering

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The rapid evolution of digital technology and the increasing integration of artificial intelligence in agriculture have paved the way for groundbreaking solutions in plant identification. This research pioneers the development and training of a deep learning model to identify three aromatic plants—rosemary, mint, and bay leaf—using advanced computer-aided detection within the You Only Look Once (YOLO) framework. Employing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, the study meticulously covers data understanding, preparation, modeling, evaluation, and deployment phases. The dataset, consisting of images from diverse devices and annotated with bounding boxes, was instrumental in the training process. The model’s performance was evaluated using the mean average precision at a 50% intersection over union (mAP50), a metric that combines precision and recall. The results demonstrated that the model achieved a precision of 0.7 or higher for each herb, though recall values indicated potential over-detection, suggesting the need for database expansion and methodological enhancements. This research underscores the innovative potential of deep learning in aromatic plant identification and addresses both the challenges and advantages of this technique. The findings significantly advance the integration of artificial intelligence in agriculture, promoting greater efficiency and accuracy in plant identification.

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“…For example, tracking methods like DeepSORT and ByteTrack have been widely applied in industry, demonstrating excellent performance in pedestrian and vehicle tracking. However, tracking small objects such as NKCs in complex backgrounds still poses many challenges [16][17][18][19]. Examples of different morphologies of natural killer cells: (a) A non-polarized natural killer cell (NP-NKC), with its intracellular substances roughly evenly distributed, presents a regular spherical shape overall.…”

Section: Introductionmentioning

confidence: 99%

An Improved Two-Shot Tracking Algorithm for Dynamics Analysis of Natural Killer Cells in Tumor Contexts

Zhou,

Tang,

2024

Bioengineering

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Natural killer cells (NKCs) are non-specific immune lymphocytes with diverse morphologies. Their broad killing effect on cancer cells has led to increased attention towards activating NKCs for anticancer immunotherapy. Consequently, understanding the motion characteristics of NKCs under different morphologies and modeling their collective dynamics under cancer cells has become crucial. However, tracking small NKCs in complex backgrounds poses significant challenges, and conventional industrial tracking algorithms often perform poorly on NKC tracking datasets. There remains a scarcity of research on NKC dynamics. In this paper, we utilize deep learning techniques to analyze the morphology of NKCs and their key points. After analyzing the shortcomings of common industrial multi-object tracking algorithms like DeepSORT in tracking natural killer cells, we propose Distance Cascade Matching and the Re-Search method to improve upon existing algorithms, yielding promising results. Through processing and tracking over 5000 frames of images, encompassing approximately 300,000 cells, we preliminarily explore the impact of NKCs’ cell morphology, temperature, and cancer cell environment on NKCs’ motion, along with conducting basic modeling. The main conclusions of this study are as follows: polarized cells are more likely to move along their polarization direction and exhibit stronger activity, and the maintenance of polarization makes them more likely to approach cancer cells; under equilibrium, NK cells display a Boltzmann distribution on the cancer cell surface.

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Small Object Detection and Tracking: A Comprehensive Review

Cited by 27 publications

References 93 publications

Lightweight Detection Methods for Insulator Self-Explosion Defects

Lightweight Detection Methods for Insulator Self-Explosion Defects

Model Development for Identifying Aromatic Herbs Using Object Detection Algorithm

An Improved Two-Shot Tracking Algorithm for Dynamics Analysis of Natural Killer Cells in Tumor Contexts

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