How do you handle small objects in object detection?

How do you handle small objects in object detection?

In this post, we have discussed a few strategies for improving your small object detector, namely:

1. Increasing your image capture resolution.
2. Increasing your model’s input resolution.
3. Tiling your images.
4. Generating more data via augmentation.
5. Auto learning model anchors.
6. Filtering out extraneous classes.

What is small object detection?

Small object detection is an interesting topic in computer vision. Specifically, we run models with different backbones on different datasets with multiscale objects to find out what types of objects are suitable for each model along with backbones.

Which model is best for small object detection?

It was found that ResNet-50 showed the best results. They have chosen the best anchor sizes that fit the dataset they have been testing the network on. Also, as well as in the previous paper about finding tiny faces, it was shown that using context around the objects significantly helps in detection.

What are the types of object recognition?

Top 8 Algorithms For Object Detection

• Fast R-CNN.
• Faster R-CNN.
• Histogram of Oriented Gradients (HOG)
• Region-based Convolutional Neural Networks (R-CNN)
• Region-based Fully Convolutional Network (R-FCN)
• Single Shot Detector (SSD)
• Spatial Pyramid Pooling (SPP-net)
• YOLO (You Only Look Once)

Can faster RCNN detect small objects?

Detection performance is significantly better than Faster R- CNN. Therefore, our algorithm is an effective way to detect small objects.

Is object detection a solved problem?

In recent years classification models have surpassed human performance and it has been considered practically solved. While there are plenty of challenges to image classification, there are also plenty of write-ups on how it’s usually solved and which are the remaining challenges.

Why is object recognition difficult?

Visual object recognition is an extremely difficult computational problem. The core problem is that each object in the world can cast an infinite number of different 2-D images onto the retina as the object’s position, pose, lighting, and background vary relative to the viewer (e.g., [1]).

Which method is best for object detection?

Most Popular Object Detection Algorithms. Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO (You Only Look Once). The R-CNN’s are in the R-CNN family, while YOLO is part of the single-shot detector family.

Which is better small object detection or state of the art?

Through testing, the detection accuracy of our model for small objects is 11% higher than the state-of-the-art models. In addition, we also used the model to detect aircraft in remote sensing images and achieved good results. 1. Introduction

What causes small objects to not be detected?

Small object dataset. Usually, since small objects have low resolution and are near large objects, small objects are often disturbed by the large objects and it leads to failure in being detected in the automatic detection process. As the mouse in Figure 1 is often placed next to the monitor, the common saliency detection model [ 11, 12

Can a small object show as a large object?

From the above object category, we can find that the actual size of most objects in the dataset is large object. Even if there are some small objects, such as bottles, these small objects display very large objects in the image because of the focal length.

How is object detection used in the military?

Object detection is widely used in intelligent monitoring, military object detection, UAV navigation, unmanned vehicle, and intelligent transportation. However, because of the diversity of the detected objects, the current model fails to detect objects.