Detection of small objects by image pyramids and high-resolution feature maps in image detection

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Detection of small objects by image pyramids and high-resolution feature maps in image detection

Detecting small objects is generally a difficult task in image detection. Because small objects have few pixels, their features may be obscured and difficult to capture with normal resolution feature maps, making the use of image pyramids and high-resolution feature maps an effective approach in such cases.

Below we discuss some important concepts and methods related to the detection of small objects.

1 Image Pyramid:

An image pyramid is a set of resized versions of the original image at different resolutions. At the bottom of the pyramid is the original image, and the higher up you go, the lower the resolution. This allows objects to be detected at different resolutions, with smaller objects being captured at higher resolutions and larger objects at lower resolutions.

2. high-resolution feature map:

A high-resolution feature map is a feature map generated at the output layer of the network that contains information close to the resolution of the original image. This allows for the extraction of detailed information about small objects. Typically, the feature map from the last layer of a convolutional neural network (CNN) is used, as described in “CNN Overview and Algorithm and Implementation Examples.

3. multi-stage object detection:

Multi-stage object detection architectures (e.g., Faster R-CNN as described in “Overview, Algorithms, and Examples of Implementations of Faster R-CNN” and YOLO as described in “Overview, Algorithms, and Examples of Implementations of YOLO (You Only Look Once)“) can use different scale feature maps to detect objects. These architectures improve detection of small objects by combining different resolutions of features.

4. scale sensitive detectors:

The use of scale sensitive detectors is especially important for detecting small objects. Scale sensitive detectors can detect objects of different scales simultaneously and integrate multiple feature maps.

5. Non-Maximum Suppression:

Non-maximum suppression is used to refine detection results. This method selects the most reliable of multiple detections and eliminates duplicate detections. For more information on NMS, see also “Overview of the Non-Maximum Suppression (NMS) Algorithm and Examples of Implementations.

In detecting small objects, a combination of these methods is commonly used, as well as the training process of the model, including data expansion, adjustment of the learning rate, and selection of an appropriate loss function. Balancing the training data to include small objects also helps.

Algorithms used in image detection for detecting small objects with image pyramids and high-resolution feature maps

Several algorithms and approaches exist for using image pyramids and high-resolution feature maps in the detection of small objects. The main algorithms and approaches are described below.

1. Faster R-CNN:

Faster R-CNN is an architecture that uses convolutional neural networks (CNN) for object detection. This architecture uses Region Proposal Network (RPN) to propose candidate regions with feature maps of different scales. This enables detection of small objects, and by combining feature maps of different scales, high-resolution information can also be used. See also “Overview of Faster R-CNN, Algorithms and Examples of Implementations” for details.

2. YOLO (You Only Look Once):

YOLO is an algorithm that enables real-time object detection and has the ability to detect objects at different scales. See “YOLO (You Only Look Once) Overview, Algorithm, and Example Implementation” for more details.

3. Single Shot MultiBox Detector (SSD):

SSD is one of the algorithms that successfully detects objects at different scales by simultaneously predicting the location and class of objects from feature maps at several different resolutions, thus enabling the detection of even small objects. For more information, see “SSD (Single Shot MultiBox Detector) Overview, Algorithm, and Example Implementation.

4. RetinaNet:

RetinaNet is an object detection algorithm that uses a loss function called Focal Loss and is a powerful method for detecting small objects. See “RetinaNet Overview, Algorithm and Example Implementation” for more information.

5. EfficientDet:

EfficientDet applies the EfficientNet efficient model architecture described in “About EfficientNet” to object detection, and is a method that detects objects at different scales and focuses on small objects. The algorithm offers high performance and efficiency. For more information, see “EfficientDet Overview, Algorithms, and Example Implementations.

Although these algorithms offer different approaches to small object detection, the use of image pyramids and high-resolution feature maps are often used in combination to improve small object detection. In particular, these approaches can be very effective when objects appear at different scales and resolutions in the image.

Examples of Implementations of Detecting Small Objects with Image Pyramids and High-Resolution Feature Maps in Image Detection

Example implementations using image pyramids and high-resolution feature maps for small object detection are typically found in convolutional neural network (CNN)-based object detection frameworks. Below is an overview of an example implementation using Python and PyTorch. This example implementation is based on Faster R-CNN.

import torch
import torchvision
from torchvision.models.detection import FasterRCNN
from torchvision.transforms import functional as F
from PIL import Image

# Load Model
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()

# Loading Images
image_path = 'sample.jpg'
image = Image.open(image_path)

# Image Pyramid Settings
min_size = 600  # Minimum resolution
max_size = 1000  # Maximum resolution
im_size = min(image.size)
im_scale = float(min_size) / float(im_size)
if max_size is not None and im_scale * im_size > max_size:
    im_scale = float(max_size) / float(im_size)

# Applying the Image Pyramid
image = F.resize(image, int(im_size * im_scale))
image_tensor = F.to_tensor(image)
image_tensor = image_tensor.unsqueeze(0)

# Acquisition of high-resolution feature maps
with torch.no_grad():
    output = model(image_tensor)
    high_res_feature_map = output[0]['feats']

# Object Detection
predictions = model(image_tensor)

# Display Results
print(predictions)

In this example, the following steps are performed

Load the Faster R-CNN model and use the pre-trained model.
Apply image pyramid and resize the input image to different resolutions. This allows detection of small objects.
Obtain a high-resolution feature map. The object detection model generates a high-resolution feature map in parallel with detection.
Perform object detection and obtain predictions of detected objects.

The challenges of detecting small objects with image pyramids and high-resolution feature maps in image detection.

Several challenges and limitations exist when using image pyramids and high-resolution feature maps to detect small objects. The main challenges are described below.

1. computational cost:

The use of image pyramids increases the computational cost of processing images at different scales. In particular, generating a large number of high-resolution images can increase resource consumption and processing time.

2. memory usage:

High-resolution feature maps typically require a lot of memory. It can be difficult to hold all high-resolution feature maps simultaneously, especially when GPU memory is limited.

3. overfitting:

The use of image pyramids and high-resolution feature maps can cause the model to overfit small objects. Overfitting can adversely affect general performance.

4. detector design:

The design of algorithms that combine image pyramids and high-resolution feature maps is complex and requires appropriate hyperparameter tuning. Expertise in model architecture and detector design is required.

5. data balancing:

The training dataset must have an adequate sampling of small objects; if there are few small objects, the model may not train properly.

6. excessive candidate regions:

The use of image pyramids may generate a large number of candidate regions. Methods such as non-maximum suppression (NMS) are needed to address this.

Addressing the Challenges of Detecting Small Objects with Image Pyramids and High-Resolution Feature Maps in Image Detection

To address the challenge of detecting small objects using image pyramids and high-resolution feature maps, the following methods may be considered

1. model optimization:

Select a lightweight model architecture: Adopting a lightweight model architecture specialized for small object detection can reduce computational cost.” Lightweight models such as MobileNet described in “About MobileNet” and EfficientNet described in “About EfficientNet” are suitable for detecting small objects.
Pruning: Reduces unnecessary parameters in the model to make the model lighter. This reduces computational cost.

2. data enhancement:

Address sample imbalance: Focus on small objects in the training dataset and perform data expansion to increase the frequency of small objects to reduce sample imbalance. Data extensions that accommodate various scales, rotations, and deformations will also be useful. For more information on data extension techniques, see “Small Data Machine Learning Approaches and Examples of Various Implementations.

3. adjusting hyperparameters:

Image pyramid settings: Tune hyperparameters related to image pyramid scaling method, range, and resolution to optimize the trade-off between computational cost and performance.
adjust learning rate: prevent over-fitting by setting an appropriate learning rate schedule during training with image pyramids and high-resolution feature maps.

4. adjusting Non-Maximum Suppression (NMS):

Adjusting NMS Threshold: Setting a high NMS threshold reduces duplicate detections and narrows the detection results. However, it is important to select an appropriate threshold.

5. ensemble learning:

Combining results from multiple models: Combining several different models or architectures improves detection performance for small objects. Ensemble learning is done by ensemble averaging and voting. See also “Ensemble Learning: Overview, Algorithms, and Examples” for more details.

6. hardware acceleration:

Hardware acceleration, such as GPUs and TPUs, can be used to speed up the processing of high-resolution feature maps and reduce computational cost. See “Hardware in Computers” for more information.

7. advanced data preprocessing:

Using information about the scale of objects: One can consider how to efficiently generate candidate object regions by utilizing information about the scale of objects. See also “Noise Removal, Data Cleansing, and Missing Value Interpolation in Machine Learning” for details.

Reference Information and Reference Books

For details on image information processing, see “Image Information Processing Techniques.

Reference book is “Image Processing and Data Analysis with ERDAS IMAGINE“

“Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data“

“Introduction to Image Processing Using R: Learning by Examples“

“Deep Learning for Vision Systems“