Questions tagged [object-detection]

Object detection deals with recognizing the presence of objects of a certain semantic class (e.g., “humans”, “buildings”, “cars”, etc.) in digital image and video data.

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How do you approach a CNN problem?

I wanna create a machine learning model based on a region based CNN architecture (either RCNN, Fast RCNN or Faster RCNN). As an framework I wanna use Pytorch. I made a image containing apples and ...
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Total Loss Going Up after First Checkpoint with LayoutLM [closed]

I'm trying to finetune LayoutLM V3 Base model using the provided dit/train_net.py script on my own custom dataset that is similar to PubLayNet. The learning starts ...
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YOLO v2 loss function

I'm trying to understand (and implement) the YOLOv2 loss function, which is not given explicitly in the original paper. There are several posts on this topic, but quite a few seem to confuse the ...
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Resizing images for object detection using Faster R-CNN

I would like to detect several largest objects and extract features from images using Faster R-CNN. Given that Faster R-CNN was trained on images with the shorter side of 600 pixels. Does it make ...
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Differences between region proposal and bounding box in object detection

Is it correct to set the term region proposal equals to the term bounding box? I am not sure if the bounding box is only the box which detects an real object in an image and if region proposals are ...
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Balancing a dataset in object detection through data augmentation and then random oclusion of classes?

first of all some info about the model I'm using. I'm using YOLOR, which uses a YOLOV5 formatted dataset. I'm trying to detect components off of PCB's which are divided into some groups (visible on ...
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Why do we sort confidence scores when calculating MAP in Object Detection?

This question is specific to object detection's metric Mean Average Precision. I sieved through multiple articles like the one by Jonathon and here by Kaggler Tito. One part I cannot grasp immediately ...
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Is classification with the backbone network a justifiable surrogate task in object detection?

There is a huge dataset of pictures but just a portion of the samples has bounding boxes. The main goal is to build an object detection model on the labeled dataset. One problem is that the dataset is ...
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Unconventional pretext task in computer vision - can I somehow justify it?

I was working on a industrial object detection neural network project. Since we had multiple images of the same object in different (but fixed) positions and light conditions, our dataset was very ...
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Extract Local Attention Positions

I am working on a project, I want to extract the local attention positions present in the feature maps of every level in the FPN. Below is the visualization: I am using MMDetection toolbox for ...
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What should I do with Images that has no objects?

I have a dataset that contains images that has cancerous nodules. I want to use object detection models to detect these nodules from the image by using an object detection model. Now my dataset has ...
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shape of output in feature pyramid network for RetinaNet

In FPN, each pyramid outputs a tensor which will go to classification and regression. This has an output of 256-d channels. But what is the complete shape of the output, including mini-batch size, ...
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Is Intersection over Union in object detection differentiable?

Is IoU in object detection differentiable or can be back propagated? Is it used in the training process or just in the inference?
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Annotations and Bounding Box Definition in Object Detection

I'm having confusion about the concepts of object detection. If the dataset has been annotated to have ground truth in (x, y) of the four vertices, then why are the papers (using the same dataset) are ...
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How are the region proposals processed in Fast R-CNN during training and testing?

How are the region proposals processed in Fast R-CNN during training and testing? From my understanding, during training we sample some small number (e.g. 64) of region proposals out of the ~2000 ...
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Data association on data from multiple cameras

Suppose we have several cameras that cover a certain area. In each camera we track a person. Each person have a path in global coordinates, timestamps and a feature-vector. The goal is to group these ...
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Mean average precision varying based on classes

In my dataset, I have 10 classes, which have some peculiar characteristics (e.g., a single "object" can be separated into multiple disconnected parts). Now, the goal is to perform weakly ...
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mAP_0.5 vs mAP_0.5 to 0.95

The mAP is mean average prediction and I know how to calculate about it. Also, it is used evaluation according to IoU in Tensorboard. However, I want to know specific reasons that we use two ...
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Conditional class probabilities in YOLOv1

In YOLO v1, authors defines conditional class probabilities (P(Class_i/Object)) I don't understand that authors can define the head output to conditional class probabilities. When the model finishes ...
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When is Non-Max Suppression used in Object Detection

Is non-max suppression for bounding boxes obtained from a Region Proposal Network performed during training? From what I gather, NMS is not differentiable-- in which case, it can't be performed during ...
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how to check if unknown object partly covers the object i want to detect

I want to detect an object from an image,I use yolo object detection. I detect car from the image. I consider the case when another unknown object partly covers it, how can my program know that there ...
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Object detector evaluation (multi-class)

I have a (long) question regarding the evaluation of object detectors. I have two separate networks, the first one detects relevant objects (-> bounding boxes) and the second one classifies the ...
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Detecting small objects with deep learning and detectron2

I'm using Detectron 2 (https://github.com/facebookresearch/detectron2) and the R50-FPN 3x model from the model zoo in order to identify small objects (asteroids) in telescope images. After running my ...
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Doubts about object detetction metrics

i'm confused about this metrics. I want to calculate the value of True Positive, False Positive and False negative in order to obtain precision, recall, accuracy and confusion matrix. The task is to ...
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How is it determined if object detection is accurate?

Average precision is a common metric used for object detection. Although average precision is from 0 to 1, 0.5 is considered good on the COCO dataset. If the only way to measure if an object detection ...
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Fine-tuning a pre-trained Tensorflow model

I have two questions regarding fine-tuning a pre-trained Tensorflow model on a mix of high-quality and low-quality images.In other words, ' Does the large contrast in image quality confuses the model ...
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Template matching for detecting multiple objects in image (+500 different categories)

I want to detect items from the inventory of a game. Detection is in real time, so I need decent performance (> 5 FPS). There are over 500 different items, but the item icons will not change so a ...
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Summary of all the results of all participants after 2015 in ImageNet (ILSVRC) challenge. Does such resource exist?

I have been reading about the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). And while I can find the paper by Russakovsky et al. from 2014(updated in 2015), which contains all the ...
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Why does strict translation invariance get destroyed after doing zero padding in the images?

While reading the paper on the Siamese Visual Tracking approach, I stumbled on this concept of Strict Translation Invariance in CNNs. The paper quotes: "Concretely speaking, one reason is that ...
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Object detection model doesn't perform well on camera frames

I am working on my thesis project, and I am a bit stuck on a problem. I am using this pretrained object detection model on a security camera, to find the b-box of objects.: http://download.tensorflow....
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Performing object detection without localization

I'm looking to build a computer vision model that can detect the presence or absence of several classes of object within an image, but the localization of these objects is not required. The end ...
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What is the difference between these error metrics in object detection

I am wondering what is the difference in these error metrics (and what they are) in object detection: mAP_0.5:0.95, mAP_0.5, recall, precision
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Yolov- 3: How are features / detection at 3 different level arranged to give 1 final output?

In yolo-v3, at layer numbers 82,94,106, detection are made. What if Same object was detected in all those places? Grids were different due to no of grids ...
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Does it help to provide training samples with no target objects in object detection?

I am training a Yolo for object detection. I was wondering if it will help to provide images with no instances of the target object present. If so, what would the VOC label of such an instance look ...
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what does it mean that the effective receptive field to be large for Region Proposal Network in Faster R-CNN?

I am trying to understand the Region Proposal Network portion (RPN) of the faster R-CNN paper. Under section 3.1, the authors mention the justification for using a 3x3 sliding network over the ...
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Mean Average Precision (MAP) object detection metric

I am reading up on object detection metrics from this github repository. I have a slight confusion regarding the precision x recall curve mentioned under the Metrics heading. It says that The ...
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Card image segmentation

I have a dataset of images with a single rectangular (credit) card in the middle. My goal is to filter out the pixels of the card. I would say the particular difficulties of this task are that the ...
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CNN Color Invariance

To detect a specific object that can have any color, if we train our model with images containing that object with just a few colors (e.g. 2 or 3 colors), will the performance of our model's ...
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YoloV3: Training set, true label boxes vs anchor boxes

I understand the anchor boxes as a set of boxes that based on a comprehensive exploration of the data ( all the true bonding boxes) best describe the variable sizes of all true boxes in the training ...
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Is there any statistically meaningful definition for object confidence in object detection?

Most modern object detection algorithms rely on neural networks and output a bounding box and confidence for each object (or more accurately, a confidence for each possible object class considered, ...
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What are "Grids" and Detection at different scales" in YOLOV3?

I've recently started working with Yolov3 and the more I go in depth, the more confused I get. In the simplest terms what I think about YOLOV3 ...
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Can I conclude from object detection metrics on temporal behavior of a detection system?

Assuming I know typical object detection metrics like precision, recall etc. for a certain class, is there anyway to conclude on the probability that an object of that class which is present in ...
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What is the average precision in the case of no positives for a given category in the context of object detection

In attempting to calculate the average precision of an object detection model, I am wondering about an edge case. Suppose at evaluation time that for a given category, that no detections of that ...
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When computing mAP for an object detection model, how many detections should one consider?

I am trying to write some code to evaluate the MS COCO style mAP (mean average precision, average computed at the category level) at different IOU levels in the context of object detection with a ...
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Two True Positive for one ground truth in object detection

I am wondering is it possible to have two true positive predictions for one bounding box ground truth only. Following this section from Stanford. They define truth positive like this: We start with ...
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Enemy detection in 3D video games without labeled dataset

I'm working on a tool for single-class object detection in video games, which should be able to detect enemies in 3D environments. The main problem is, that I cannot use any labeled dataset. Instead I ...
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Evaluate result of a Detection algorithm based on the object's shape as Ground truth

I'm working on project where i have to detect small colored cars driving on rails from static camera (Bird's Eye-view see below image) The algorithm in short outputs first a mask image with only the ...
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Resize image in object detection task of computer vision

In object detection, they usually resize by keeping the ratio the same as the original image, which usually names "letterbox" resize. My question is: Why we need to do that? As I see with ...
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How many images with bounding boxes are in the ImageNet object detection dataset?

I am trying to understand how many images with object-detection bounding boxes are in ImageNet dataset, and how many objects are there in these images. In MS-COCO paper, it says: "Recently, a ...
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Should I keep training samples without labels

I want to supervisely train an object detector for pedestrians, this detector is expected to ideally detect all the pedestrians in an image, and output the detected pedestrians as 2D bounding boxes, ...
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