"Object detection is one of the most exciting and challenging problems in computer vision, and deep learning has emerged as a powerful tool for solving it."
—Dr. Liang-Chieh Chen
Object detection is a fundamental task in computer vision, which involves identifying and localizing objects in images. Deep learning has revolutionized object detection, making it possible to detect objects more accurately and efficiently in images and videos. In 2023, several deep learning models are making significant progress in object detection. Here are the top 10 deep learning models for object detection in 2023:
1. YOLOv7
YOLOv7, or You Only Look Once version-7, is a state-of-the-art deep learning model for object detection. YOLOv7 is based on the original YOLO architecture, but uses a more efficient backbone network and a new set of detection heads. YOLOv7 can detect objects in real time with high accuracy and can be trained on large datasets. The model is very efficient and can run on low-end devices.
advantage:
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Object detection is fast and efficient
High accuracy on large datasets
Works on low-end devices
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shortcoming:
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May have difficulty detecting small objects
Requires large datasets for best performance
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Remarks: As of the publication of this article, YOLOv8 improved by ultralytics has been released, but it is still in the process of rapid "optimization". For details, please check: https://github.com/ultralytics/ultralytics
2. EfficientIt
EfficientDet is a deep learning model for object detection that uses an efficient backbone network and a new set of HEADs. EfficientDet aims to achieve efficient and accurate object detection and is capable of detecting objects with high accuracy in real-time. The model achieves state-of-the-art results on several benchmark datasets and can be trained on large datasets.
advantage:
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Achieves state-of-the-art performance on several benchmark datasets
Efficient and Accurate Object Detection
Can be trained on large datasets
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shortcoming:
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Requires a lot of computing resources
Training on smaller datasets can be challenging
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3. RetinaNet
RetinaNet is a deep learning model for object detection that uses a feature pyramid network and a new focal loss function. RetinaNet aims to solve the problem of imbalanced foreground and background examples in object detection, thereby improving accuracy. The model is efficient and can run on low-end devices, making it a popular choice for real-time object detection.
advantage:
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Improved object detection accuracy
Efficient and can run on low-end devices
easy to train and use
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shortcoming:
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May have difficulty detecting small objects
Requires large amounts of data for optimal performance
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4. Faster R-CNN
Faster R-CNN is a deep learning model for object detection that uses a region proposal network to generate candidate object locations. The model then uses a second network to classify and positionally refine the proposal's regions. Faster R-CNN is known for its high accuracy and is often used for object detection in images and videos.
advantage:
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Object detection with high accuracy
Effective for object detection in images and videos
easy to train and use
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shortcoming:
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Can be computationally expensive
Can be slow when detecting objects in real time
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5. Mask R-CNN
Mask R-CNN is a deep learning model for target detection that extends Faster R-CNN to predict target MASK. The model uses a third network to generate a pixel-level mask for each detected object. Mask R-CNN is known for its high accuracy in object detection and instance segmentation.
advantage:
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High accuracy in object detection and instance segmentation
Pixel-level MASK can be generated for each detected object
easy to train and use
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shortcoming:
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Can be computationally expensive
Can be slow when detecting objects in real time
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6. CenterNet
CenterNet is a deep learning model for object detection that uses heatmaps to predict the center of each object. The model then uses a second network to predict the object's size and orientation. CenterNet is known for its high accuracy and efficiency in object detection and achieves state-of-the-art results on several benchmark datasets.
advantage:
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Achieves state-of-the-art results on several benchmark datasets
Object detection with high accuracy and efficiency
Can handle occlusions and small targets
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shortcoming:
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Can be computationally expensive
May not handle highly overlapping targets well
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7. DETR
DETR, or Detection Transformer, is a deep learning model for target detection that uses a Transformer-based architecture. The model uses an ensemble prediction method to simultaneously predict the class and location of each object. DETR is known for its high accuracy and simplicity since it does not require anchor boxes or non-maximum suppression.
advantage:
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High accuracy and simplicity for object detection
Can handle highly overlapping targets
No need for anchor boxes or non-maximum suppression
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shortcoming:
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Can require significant computing resources
Requires large amounts of data for optimal performance
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8. Cascade R-CNN
Cascade R-CNN is a deep learning model for object detection that uses cascaded R-CNN networks to improve the accuracy of object detection. The model progressively reduces false and missed detections in each stage of the cascade. Cascade R-CNN is known for its high accuracy and achieves state-of-the-art results on several benchmark datasets.
advantage:
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Achieves state-of-the-art results on several benchmark datasets
High accuracy of object detection
Can handle small and occluded targets
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shortcoming:
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Can require significant computing resources
Requires large amounts of data for optimal performance
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9. SSD
SSD, or Single Shot MultiBox Detector, is a deep learning model for object detection that uses a single network to predict the location and category of objects. The model detects objects at different scales using a feature pyramid network and achieves high accuracy in object detection. SSDs are also known for their high efficiency and can run in real time on low-end devices.
advantage:
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High accuracy and efficiency of object detection
Real-time object detection on low-end devices
easy to train and use
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shortcoming:
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May not detect small objects well
May require large amounts of data for optimal performance
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10. FCOS
FCOS, or Fully Convolutional One-Stage Object Detection, is a deep learning model for target detection that uses a fully convolutional architecture to predict the category and location of each target. The model is efficient and highly accurate, and achieves state-of-the-art results on several benchmark datasets. FCOS is also known for its simplicity as it does not require anchor boxes or non-maximum suppression.
advantage:
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Achieves state-of-the-art results on several benchmark datasets
High accuracy and efficiency of object detection
No need for anchor boxes or non-maximum suppression
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shortcoming:
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Can require significant computing resources
Large amounts of data are required to achieve optimal
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