Questions tagged [object-detection]

For questions related to object detection (where objects can be e.g. humans, dogs, houses, etc.), whose meaning or definition can vary depending on the context. OD can refer to the task of locating (i.e. finding the coordinates) an object in an image (so, in this case, it would be a synonym for object localization) or the task of locating the object and classifying it (i.e. object localization + object classification).

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5 views

What are the main differences between YOLOv3 and RetinaNet object detection algorithms?

I am looking at a certain project that compares performance on a certain dataset for an object detection problem using YOLOv3 and RetinaNet (or the "SSD_ResNet50_FPN" from TF Model Zoo). ...
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Towards Faster and Applicable Object Tracking

Recently trying some algorithm for object tracking such DeepSORT and centroid tracker. However, these trackers are slow to run on mobile device EVEN on a single GPU laptop, does anyone know the light, ...
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Instance Segmentation Using A Semantic Segmentation Map and Class-Wise Bounding Boxes

Is it possible to perform instance segmentation if you have the following: Binary Segmentation Map Bounding Boxes (with respective class) Let's say we're doing something within cellular microscopy ...
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What are the state-of-the-art Person-Detektion / Human-Segmentation?

I would like to use a deep learning approach to detect people in videos. I have found some freely accessible implementations like Human Segementation with Pytorch or BodyPix / DeepLab / Pixellib with ...
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20 views

Is there a methodology for splitting up annotated orthophotos into smaller photos that retain the original bounding boxes?

I'm trying to train an object detection algorithm (i.e. YOLOv4 Scaled, Faster R-CNN) on data taken from large orthophotos. Let's say I have one class, and I label the entire orthophoto with bounding ...
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51 views

How to treat (label and process) edge case inputs in machine learning?

In every computer vision project, I struggle with labeling guidelines for border cases. Benchmark datasets don't have this problem, because they are 'cleaned', but in real life unsure cases often ...
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Improving Mask RCNN by arbitrary scaling head input

Currently, I am looking at how Mask R-CNN works. It has a backbone, RPN, heads, etc. The backbone is used for creating the feature maps, which are then passed to the RPN to create proposals. Those ...
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How does the region proposal algorithm in R-CNN work? [duplicate]

I'm trying to understand R-CNN, but I'm a bit lost in the first stage (region proposal). Correct me if I'm wrong, but as far as I understand, there is an algorithm that proposes regions in the image ...
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How are Ground truth provided to each Pyramid map in RetinaNet or YOLOv3 Paper? How is the mapping of Feature Pyramids done to Ground Truth

SO the YOLO V3 and RetinaNet both uses the Feature pyramids which look something like this: (except b and e which have one ...
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Object detection approaches without anchors and NMS

The Context From all of the problems I have worked with in computer vision, the most challenging one is the object detection. This is not because the problem itself is complex to understand or bad ...
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33 views

Can object detection approaches be used to solve text/detection problems?

I have been working on text detection and recognition for almost two months and new on this field. So far, I have fine-tuned, tested, and trained several text detection/recognition methods, such as ...
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Nonrealtime tracking for object detection (with or without deep features)

I've been doing a search for tracking systems based on bounding box for several days (like Sort, DeepSort, Motpy, ...). But neither is nonrealtime. My problem is based on the tracking of multiple ...
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31 views

Bounding Box Regression - An Adventure in Failure

I've solved many problems with neural networks, but rarely work with images. I have about 18 hours into creating a bounding box regression network and it continues to utterly fail. With some loss ...
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Aggregating 2D object detections into 3D object detections

I have a data set of 3D images with some bounding box annotations. The images are too large to train something like YOLO 3D (would run out of memory), so I instead created slices of the 3D images with ...
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23 views

Is it possible to modify or replace the basic network of YOLO?

I have an idea to adapt YOLO algorithm to my application, the original YOLO algorithm is for image classifications, which have 24 convolutional layers with output class of 1000, is it possible to ...
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Which AI technique should I use for (key)point detection (in an image of a plantar pressure)?

I am relatively new to the field of AI. I have a problem that I would like to solve with AI, but I don't know which buzzwords I should use to search for solutions. I have a plantar pressure scan, like ...
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How are nested bounding boxes handled in object detection (and in particular in the case of the SSD)?

The basic approach to non-maximum-suppression makes sense, but I am kind of confused about how you handle nested bounding boxes. Suppose you have two predicted boxes, with one completely enclosing ...
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What ensemble methods are used in the state-of-the-art models?

What ensemble methods are used in the state-of-the-art models? When I surveyed the state-of-the-art methods of classification and detection, e.g. ImageNet, COCO, etc., I noticed that are few or even ...
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What are the best metrics for Multi-Object Tracking (MOT) evaluation and why?

I want to compare multiple computer vision Multi-Object Tracking (MOT) methods on my own dataset, so first I want to choose the best metrics for this task. I have carried out some research in ...
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Object Detection as a means of Anomaly Detection

Is it possible to train an Object Detector (e.g. SSD), to detect when something is not in the image. Imagine an assembly line that transports some objects. Each object needs to have 5 screws. If the ...
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Face detection and replacement in photos

I have 2 photos, and my goal is to detect the face in one and place it on the face of the person in the other photo- basically face detection and replacement. It's not deep fakes. It's more of a ...
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Is it possible to improve the average precision of YOLO trained on Open Images Dataset by fine-tuning it with COCO?

I consider pre-training a YOLOv5 with Google Open Images Object Detection dataset. The dataset includes general domain categories with ~15 M box samples. After the pre-training is done, I will fine-...
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38 views

How to compute dominant colors in an image?

I was trying Google Cloud's Vision API, and how the dominant colors part shows. I uploaded a sample image, and here is the results for the dominant colors. I realized it doesn't simply count pixel ...
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Object detection noise filtering

In my project, I am detecting only one class, which is "airplane", using yolov5. However, at some frames, the neural network labels some of the buildings as airplanes, which obviously are ...
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If random rotations are included in the data augmentation process, how are the new bounding boxes calculated?

When studying bounding box-based detectors, it's not clear to me if data augmentation includes adding random rotations. If random rotations are added, how is the new bounding box calculated?
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How is the shape of the anchor boxes predefined in YOLO algorithm?

I am not sure if I really understand how anchor boxes are defined. As far as I understand, in YOLO algorithm you define a set of "good" shapes (anchor boxes) that may contain the object you ...
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When training deep learning models for object detection in images, do you need a large number of images, or a large number of training samples?

I am training a deep learning model for object detection. The consensus is that the more images that you have, the better the results will be. All the tutorials that I have seen say that more images ...
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How to verify classification model trained on classification dataset on a detection dataset for classification purpose?

I am working on a problem that involves two tasks - detection and classification. There is no single dataset for both tasks. I am training two models, separate on detection dataset and another on ...
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Will changing the dimension reduction size of a neural network (i.e. SSD ResNet-50) change the overall outcome and accuracy of the model?

I am training a convolutional neural network to detect objects (weeds amongst crops, in my case) using TensorFlow. The original dimensions of the raw training photos are 4000 x 3000 pixels, which must ...
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Find object's location in an area using computer vision

I'm trying to see how to detect the location of a soccer ball in the field using the live camera. What are some ways to achieve this? 1- Assuming we have a fixed camera with a wide shot. How to find ...
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Get object's orientation or angle after object detection

I'm trying to get a detected car's orientation when object detection is applied. For instance, when we apply object detection on a car and get a bounding box, is there any ways or methods to calculate ...
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1answer
53 views

Why do we resize images before using them for object detection?

In object detection, we can resize images by keeping the ratio the same as the original image, which is often known as "letterbox" resize. My questions are Why do we need to resize images? ...
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Getting bounding box/boundaries from segmentations in UNet Nuclei Segmentation

From my understanding, in a tissue where nuclei are present and need to be detected, we need to predict bounding boxes (either rectangular/circular or in the shape of the nucleus, i.e. as in instance ...
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28 views

Which is more accurate between HRnet-w48 and HigherHRnet-w48 for single person pose estimation?

When I started to look into the deep High-Resolution Net paper, I came to know about the HigherHRNet and the Bottom Up HRNet as well. But I can't find any metrics or proof on which is better for a ...
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30 views

Training Object Detection model on just 10 images

I am trying to train an object detection model using Mask-RCNN with Resnet50 as backbone. I am using the pre-trained models from PyTorch's Torchvision library. I have only 10 images that I can use to ...
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1answer
74 views

How can I determine whether a video's frame is realistic (was recorded by a camera) or contains computer-generated graphics?

Given a video, I'm trying to classify whether it is a graphical (computer-generated) or realistic scene. For instance, if it contains computer-generated graphics, credit, moving bugs, blue screen, etc....
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1answer
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Are there any known models/techniques to determine whether a person in a store is a customer or a store representative?

Are there any known models/techniques to determine whether a person in a store is a customer or a store representative? For example, customer representatives can wear uniforms and then one possible ...
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What is the difference between text-based image retrieval and natural language object retrieval?

I'm working on creating a model that locates the object in the scene (2D image or 3D scene) using a natural language query. I came across this paper on natural language object retrieval, which ...
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1answer
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Is training a CNN object detector on an image containing multiple targets that are not all annotated will teach it to miss targets?

I want to train a convolutional neural network for object detection (say YOLO) to detect faces. Consider this image: In this training image, I have many people, but only 2 of them are annotated. Is ...
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Machine Learning Techniques for Objects Location/Orientation in Images

what Machine Learning tool can understand in which location and orientation a picture was taken from? That is from pictures of similar objects, say for example pictures of car interiors. So given a ...
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1answer
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Should I prefer cropped images or realistic images for object detection?

I am new to the field of AI but due to the high level of abstraction that comes with services such as Google VisionAI I got motivated to write an application that detects symbols in photos based on ...
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Formal definition of the Object Detection problem

For many problems in computer science, there is a formal, mathematical problem defition. Something like: Given ..., the problem is to ... How can the Object Detection problem (i.e. detecting objects ...
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Train 3D object detection model for custom object

I am trying to train a model that can detect a 3D object and give me a 3D bounding box around it. For this, I have a RGBD camera and a 2D LiDAR. Most of the research is done for cars/cyclists/...
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How do I label images for deep learning classification?

I have roughly 30,000 images of two categories, which are 'crops' and 'weeds.' An example of what I have can be found below: The goal will use my training images to detect weeds among crops, given an ...
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28 views

Keeping track of multiple faces throughout a video

I have a video where multiple persons are seated. I need to keep track of the emotions they show throughout the video. My final result should be a csv file with all the emotions depicted by each ...
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Object detection using CNN model architectures

I've used LabelImg to create labels for my images using YOLO. After that, I would like to input the images and labels into a CNN model, like a VGG or ResNet. I've searched a lot and have not found ...
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Detect and overlay a black TV on a video

I have a short video where a TV is shown. I need to detect the TV there (let's assume it's all pure black or it's covered with a green screen), and overlay a video on top of it on the same area. I ...
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1answer
70 views

What do we mean by 'principal angle between subspaces'?

I came across the term 'principal angle between subspaces' as a tool for comparing objects in images. All material that I found on the internet seems to deal with this idea in a highly mathematical ...
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How should I label images to get high accuracy with YOLO?

I am new to Object Detection with Yolo and I have questions regarding the labeling (for custom objects): Is there any guideline or tips on how to label images to have high accuracy at the end? ...
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Faster RCNN-RPN Network Training

I am trying to understand RPN network in Faster RCNN. I understand the concept of RPN network, Pass the input images to the pre trained CNN, and get the output as feature maps Make fixed size of the ...