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).

Filter by
Sorted by
Tagged with
0
votes
0answers
18 views

Which neural network architecture to use to detect very close and very small blobs in high resolution fluorescence images?

Context I am developing a pipeline to automate the detection of small, almost circular, bright blobs (4px) (see first image below) on high-resolution fluorescence images (2048px) and later to assign ...
-1
votes
0answers
27 views

Is there any difference between one stage and single shot object detectors? [closed]

I am confused between meaning of word "single shot" and "one stage". Do they both mean same thing?
0
votes
0answers
20 views

How does bipartite matching work in DETR?

I was going through the DETR paper to understand this end-to-end detection transformer used for object detection, and I came across this bipartite matching thing. I searched a little bit, but I was ...
0
votes
1answer
26 views

Tensorflow object detection model total loss starts out good, but suddenly explodes up to high loss numbers

I'm training a Tensorflow object detection model with approx. 7500 images of two classes, which contains approx. 10,000 classes per class. I'm using Tensorflow 2.6.0, in case that is relavent. I am ...
0
votes
1answer
33 views

How to handle an unbalanced dataset when training object detection algorithms?

I am training an object detection model, and I have some very highly unbalanced data annotations. I have almost 11,000 images, all with dimensions of 1024 $\times$ 1024. Within those images I have the ...
0
votes
0answers
25 views

How big should the dataset for retraining ssd_mobilenet_v2 be?

I have retrained ssd_mobilenet_v2 using my own dataset with 2 classes (pen or pencil), using object detection API. For my project, I expect users to select specific pencils from all pencils and ...
0
votes
0answers
10 views

Role of confidence or classification score in object detection mAP metrics

I know that mAP (mean Average Precision) is the common evaluation metric for the object detection tasks. It uses IoU (Intersection over Union) threshold such as mAP@0.5 to evaluate whether the ...
0
votes
1answer
9 views

What does a value of -1.000 mean in MS COCO Metrics for Object Detection

I am training some Object-Detection-Models from the TensorFlow Object Detection API and got from the evaluation with MS COCO metrics the following results for Average Precision: IoU = 0.5;0.9 maxDets =...
0
votes
0answers
35 views

How to get bounding box coordinates of all detected objects in yolov5 object detection?

While there is a similar question on stackoverflow, it pertains to yolov4 which, unlike yolov5, uses darknet. Yolov5 is far more intuitive to use than v4 and so a solution to this in v5 is desirable. ...
0
votes
0answers
20 views

How to improve detection of wide objects?

I am working on a project where part of it is to detect PV module arrays, I trained few object detection models through TensorFlow Object Detection API and the problem I got is that the trained models ...
0
votes
1answer
34 views

Is it possible that the fine-tuned pre-trained model performs worse than the original pre-trained model?

I have downloaded a pre-trained EfficientDet D2 model (Tensorflow 2.0) and trained it on some data (about 20000 images with 20 classes). I set the number of steps to 25000 and batch size to 3 (...
0
votes
0answers
11 views

How to scale Computer Vision? How to implement Emotion detection from live video feed of N different video simultaneously?

I have a pipeline based on Scaled Yolov4 detection algorithm for faces which extract faces of users and then uses a CNN to ...
0
votes
0answers
22 views

How to work deeper with YOLO v4 scaled

I am pretty new in object detection. When I did some classification using TF with pretrained models I changed some last layers and choosed how many layers I want to train. I don't really get it with ...
1
vote
0answers
9 views

Vector input to CNN for object detection

I am training a 3D object detection network (Retinanet-based as of the moment) for re-detecting tracked objects. I would like to be able to add the velocity vector of the tracked object as an input to ...
1
vote
0answers
8 views

Pretraining on ImageNet for YOLOv1

It is mentioned in the YOLOv1 paper that the authors pretrained their network on ImageNet with a resolution of (224,224), but while training for detection tasks, they use a resolution of (448,448). ...
1
vote
0answers
15 views

Looking for advice on how to train an AI object detection algorithm to recognize smaller objects than what it has been trained on

For some academic work, I am training an AI object detection algorithm (TensorFlow models) to look for specific objects (plants, in my case). I am taking photos with a hand-held camera, and am having ...
0
votes
0answers
13 views

Object localisation and window size(can’t use learning methods). Share the resources to solve this problem

Given two images($I_1$, $I_2$) and both images contain a similar object. First, find the location of the windows which contains the object for each image. For example, let $I_{n \times n}$ is a 2-D ...
2
votes
1answer
195 views

How to add negative samples for object detection?

My question is: how to add certain negative samples to the training dataset to suppress those samples that are recognized as the object. For example, if I want to train a car detector. All my training ...
0
votes
0answers
25 views

How to change number of trained layers in object detection TensorFlow models?

Training custom object detection models with TensorFlow usually means a transfer learning of pre-trained models and, if I understand it correctly, it means only training the few last layers, with ...
0
votes
0answers
10 views

Are supervised learning object recognition models appropriate for the challenge of recognizing dynamically generated terrain features in a game?

I decided to practice applying object recognition with TensorFlow for an interesting application this weekend. The application I chose was to recognize enemies in a game world, and as more of a ...
1
vote
1answer
19 views

What to do when the ROIs are smaller than $227 \times 227$ in R-CNN?

As English is not my native language, I have some hard time understanding the following sentence: Regardless of the size or aspect ratio of the candidate region, we warp all pixels in a tight ...
0
votes
0answers
11 views

How to change a single object detection network to a multiple object detection network?

I have trained a CNN network to detect a circle and approximate its centre and radius in an image. What I want to do now is detect the centre and radius of all the circles if there are multiple ...
1
vote
0answers
7 views

How can I get the images with the highest activation for a given unit?

I am new to machine learning. I am working on the pretrained AlexNet on Pytorch and i would like to visualize the receptive fields of a given unit U. To do that I am trying to give like 200K images as ...
0
votes
0answers
10 views

Inconsistent Classification Accuracy between Classification Network & Object Detection

I have been working on an object detection and classification problem, and I am having understanding a discrepancy in my results. I am try to detect and classify 2 classes. These objects are ...
0
votes
1answer
21 views

How to reject boxes inside each other with Non Max Suppression

I’m working on an object detection cnn, and having some issues with non max suppression. When I have a small box inside a large box, NMS is not rejecting the smaller, incorrect box, because its IOU is ...
0
votes
0answers
10 views

Should we create a label mean group of small nearby objects in object detection?

I'm working on object detection models and my dataset sometimes has a lot of small objects (stay far from the scene) (overlapping and nearby) which is really annoying in annotating (it's too small and ...
0
votes
1answer
34 views

Number of classes vs number of parameters/layers?

How to estimate the number of parameters in CNN for object detection? I know that there are some well-known architectures that was trained on a lot of data (AlexNet, ResNet, VGG, GoogleLeNet). But ...
0
votes
0answers
21 views

Finding an object in a large image

I am looking for an algorithm to solve the following problem. There is a very large image (road map). A small distorted part of big image is fed into the input. I want to find the location of a small ...
0
votes
0answers
21 views

Is it possible to do object detection on an object classification dataset?

I'm new to computer vision, which I find fascinating. I wonder whether it is possible or if there has been any research into going from object recognition data to object detection. In other words, ...
3
votes
1answer
47 views

How does Mask R-CNN automatically output a different number of objects on the image?

Recently, I was reading Pytorch's official tutorial about Mask R-CNN. When I run the code on colab, it turned out that it automatically outputs a different number of channels during prediction. If the ...
0
votes
0answers
11 views

How to obtain part filter anchors in DPM Detector

The DPM detector (https://cs.brown.edu/people/pfelzens/papers/lsvm-pami.pdf) uses latent-svm to train the weights of the root and part filters. During training for positive samples, it alternates ...
2
votes
1answer
66 views

When exactly am I overfitting -- contradicting metrics

I am training an object detection machine learning pipeline. Among the many metrics provided out of the box by tensorflow object detection API, I look at total_loss and DetectionBoxes_Precision/mAP@....
4
votes
1answer
83 views

Why object detection algorithms are poor in optical character recognition?

OCR is still a very hard problem. We don't have universal powerful solutions. We use the CTC loss function An Intuitive Explanation of Connectionist Temporal Classification | Towards Data Science ...
0
votes
1answer
64 views

Object Detection: Can I modify this script to support larger images (Scaled YOLOv4)?

I am looking at training the Scaled YOLOv4 on TensorFlow 2.x, as can be found at this link. I plan to collect the imagery, annotate the objects within the image in VOC format, and then use these ...
2
votes
1answer
45 views

Viola-Jones algorithm: Haar-like features, how are the features extracted?

If I have an image like this 1 2 3 4 5 6 7 8 a b c d e f g h ... And I apply a Haar-like feature with a template ...
1
vote
0answers
13 views

Advice required for identifying bone fragments in CT-scans using STL Files (3D image segmentation)

I am working on a project related to automating the procedure of manually segmenting some bones in CT scans and hopefully if everything goes alright in this stage, move on to do something more with ...
0
votes
1answer
60 views

How can Image Caption work?

I have two models and a file contains captions for images. The output of model 1 is .pkl files that contain the features of the images. Model 2 is the language model that will be trained with the ...
0
votes
2answers
47 views

Should I use U-net to label keys in a keyboard image?

This is a 600*800 image. Which algorithm/model should I use to get an image like the one below, in which each key is detected and labeled by a rectangle? I guess this is some kind of a segmentation ...
0
votes
1answer
19 views

Why do popular object detecting models output heatmaps instead of coordinators of object directly?

I think heatmap outputs of architectures like CenterNet, OpenPose, etc. can be changed to coordinator outputs, and loss functions like focal loss can be modified so they can deal with coordinators ...
1
vote
0answers
89 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). ...
0
votes
0answers
28 views

Are there any faster and more efficient object tracking algorithms other than DeepSORT and centroid tracker?

Recently, I am trying some algorithm for object tracking, such as DeepSORT and centroid tracker. However, these trackers are slow to run on mobile devices, or even on a single-GPU laptop. Does anyone ...
0
votes
0answers
17 views

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 ...
0
votes
1answer
22 views

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 ...
2
votes
1answer
23 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 ...
3
votes
1answer
64 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 ...
0
votes
0answers
59 views

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 ...
0
votes
0answers
34 views

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 ...
3
votes
0answers
27 views

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 ...
1
vote
0answers
54 views

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 ...
1
vote
0answers
45 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 ...