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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Related Experiment Video

Updated: Nov 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches.

Jose L Gómez1,2, Gabriel Villalonga1, Antonio M López1,2

  • 1Computer Vision Center (CVC), Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Spain.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

Multi-modal co-training using appearance and depth data significantly improves object detection accuracy. This approach overcomes data labeling bottlenecks, especially in domain-shifted scenarios, outperforming single-modal methods.

Keywords:
ADASco-trainingmulti-modalityself-drivingvision-based object detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Accurate Convolutional Neural Networks (CNNs) require extensive labeled data, which is costly and time-consuming to acquire.
  • Domain shifts between image sensors exacerbate the data labeling challenge, potentially necessitating sensor-specific labeling.
  • Semi-Supervised Learning (SSL) methods offer a potential solution to reduce reliance on fully labeled datasets.

Purpose of the Study:

  • To evaluate the effectiveness of co-training, an SSL technique, for generating self-labeled object bounding boxes (BBs) to train deep object detectors.
  • To assess the performance of multi-modal co-training using appearance (RGB) and estimated depth (D) image data.
  • To compare multi-modal co-training against single-modal (appearance-based) co-training.

Main Methods:

  • Implemented co-training, a semi-supervised learning approach, to generate self-labeled ground truth (GT) for object detection.
  • Utilized multi-modal data fusion, combining RGB appearance and estimated depth (D) views of images.
  • Compared performance against single-modal co-training (RGB only) in standard and domain-shifted settings.

Main Results:

  • Multi-modal co-training outperformed single-modal co-training in both standard SSL settings and virtual-to-real domain shift scenarios.
  • In virtual-to-real domain shift, Generative Adversarial Network (GAN)-based domain translation enabled both co-training modalities to achieve comparable performance.
  • The effectiveness holds even with an off-the-shelf depth estimation model on translated images.

Conclusions:

  • Multi-modal co-training is a promising approach to mitigate the data labeling bottleneck in training CNNs for object detection.
  • Leveraging both appearance and depth information enhances model robustness, particularly under domain shifts.
  • GAN-based domain translation can further improve performance in cross-domain scenarios.