BNE-DETR: Nighttime Pedestrian Detection with Visible Light Sensors via Feature Enhancement and Multi-Scale Fusion
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces the BNE-DETR model for improved nighttime pedestrian detection, enhancing feature representation and multi-scale target fusion for better accuracy in low-light conditions.
Area Of Science
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background
- Nighttime pedestrian detection is challenging due to poor visibility, noise, and varied target sizes.
- Existing models struggle with degraded features and complex environmental factors.
Purpose Of The Study
- To develop an improved pedestrian detection model (BNE-DETR) for nighttime visible light environments.
- To enhance feature representation, detail extraction, and multi-scale target fusion.
Main Methods
- Incorporated CSPDarknet backbone and a novel SECG module for enhanced feature representation.
- Introduced the AIFI-SEFN module for improved weak detail extraction and contextual fusion.
- Utilized the MANStar module with large-kernel convolutions for multi-scale feature handling.
Main Results
- Achieved 1.9% higher Precision, 2.5% higher Recall, and 1.9% higher mAP50 on the LLVIP dataset compared to RT-DETR-R18.
- Maintained low computational complexity (48.7 GFLOPs) and reduced parameters by 20.2%.
- Demonstrated robust performance and generalization across datasets.
Conclusions
- The BNE-DETR model significantly improves nighttime pedestrian detection accuracy.
- The proposed modules effectively address challenges in low-light and multi-scale detection.
- The model offers an efficient and effective solution for real-world nighttime surveillance.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
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...
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...