NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset

Morawski, Igor; Chen, Yuan; Lin, Yu‐Sheng; Hsu, Winston H.

doi:10.48550/arxiv.2110.10364

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Night Object Detection Dataset (NOD database) is a valuable resource designed specifically for object detection in low-light conditions, providing high-quality and extensive data [ 19 ]. This dataset comprises over 7000 images categorized into three groups: people; bicycles; and cars.…”

Section: Methodsmentioning

confidence: 99%

Bio-Inspired Dark Adaptive Nighttime Object Detection

Hung,

Lin

2024

Biomimetics

View full text Add to dashboard Cite

Nighttime object detection is challenging due to dim, uneven lighting. The IIHS research conducted in 2022 shows that pedestrian anti-collision systems are less effective at night. Common solutions utilize costly sensors, such as thermal imaging and LiDAR, aiming for highly accurate detection. Conversely, this study employs a low-cost 2D image approach to address the problem by drawing inspiration from biological dark adaptation mechanisms, simulating functions like pupils and photoreceptor cells. Instead of relying on extensive machine learning with day-to-night image conversions, it focuses on image fusion and gamma correction to train deep neural networks for dark adaptation. This research also involves creating a simulated environment ranging from 0 lux to high brightness, testing the limits of object detection, and offering a high dynamic range testing method. Results indicate that the dark adaptation model developed in this study improves the mean average precision (mAP) by 1.5−6% compared to traditional models. Our model is capable of functioning in both twilight and night, showcasing academic novelty. Future developments could include using virtual light in specific image areas or integrating with smart car lighting to enhance detection accuracy, thereby improving safety for pedestrians and drivers.

show abstract

Section: Methodsmentioning

confidence: 99%

Bio-Inspired Dark Adaptive Nighttime Object Detection

Hung,

Lin

2024

Biomimetics

View full text Add to dashboard Cite

show abstract

“…Non-anchor-based algorithms discard anchors and obtain box descriptions through other methods, such as YOLOv1 (You Only Look Once Version1), CornerNet [26], ExtrmeNet [27], Fully Convolutional One Stage (FCOS), etc. YOLOv1 performs the regression of target position and category for each pixel of the feature map.…”

Section: Introductionmentioning

confidence: 99%

G2Grad-CAMRL: An Object Detection and Interpretation Model Based on Gradient-Weighted Class Activation Mapping and Reinforcement Learning in Remote Sensing Images

Yin

Wang

Shafiq

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Remote sensing images contain important information such as airports, ports, and ships. By extracting remote sensing image features and learning the mapping relationship between image features and text semantic features, the interpretation and description of remote sensing image content can be realized, which has a wide range of application value in military and civil fields such as national defense security, land monitoring, urban planning, disaster mitigation and so on. Aiming at the complex background of remote sensing images and the lack of interpretability of existing target detection models, and the problems in feature extraction between different network structures, different layers, and the accuracy of target classification, we propose an object detection and interpretation model based on Gradient weighted class activation mapping and reinforcement learning. Firstly, ResNet is used as the main backbone network to extract the features of remote sensing images and generate feature graphs. Then, we add the global average pooling layer to obtain the corresponding feature weight vector of the feature graph. The weighted vectors are superimposed to output class activation maps. The reinforcement learning method is used to optimize the generated region generation network. At the same time, we improve the reward function of reinforcement learning to improve the effect of the region generation network. Finally, network dissecting analysis is used to obtain the interpretable semantic concept in the model. Through experiments, the average accuracy is more than 85%. Experimental results in the public remote sensing image description dataset show that the proposed method has high detection accuracy and good description performance for remote sensing images in complex environments.

show abstract

NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset

Cited by 2 publications

References 18 publications

Bio-Inspired Dark Adaptive Nighttime Object Detection

Bio-Inspired Dark Adaptive Nighttime Object Detection

G2Grad-CAMRL: An Object Detection and Interpretation Model Based on Gradient-Weighted Class Activation Mapping and Reinforcement Learning in Remote Sensing Images

Contact Info

Product

Resources

About