scholarly journals Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm

Mathematics ◽  
2018 ◽  
Vol 6 (10) ◽  
pp. 213 ◽  
Author(s):  
Jian Han ◽  
Yaping Liao ◽  
Junyou Zhang ◽  
Shufeng Wang ◽  
Sixian Li
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Color Image ◽  
Depth Image ◽  
Detection Scheme ◽  
Safe Driving ◽  
Decision Level ◽  
Single Sensor ◽  
Decision Level Fusion ◽  
Level Fusion

Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect.

scholarly journals Research on Dual Mode Target Detection Algorithm for Embedded Platform

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Li Zhang ◽  
Shaoqiang Wang ◽  
Hongwei Sun ◽  
Yifan Wang
Keyword(s):  
Visible Light ◽  
Real Time ◽  
Target Detection ◽  
Model Parameters ◽  
The Real ◽  
Detection Model ◽  
Embedded Platform ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion

Aiming at the problem that the embedded platform cannot meet the real-time detection of multisource images, this paper proposes a lightweight target detection network MNYOLO (MobileNet-YOLOv4-tiny) suitable for embedded platforms using deep separable convolution instead of standard convolution to reduce the number of model parameters and calculations; at the same time, the visible light target detection model is used as the pretraining model of the infrared target detection model and the infrared target data set collected on the spot is fine-tuned to obtain the infrared target detection model. On this basis, a decision-level fusion detection model is obtained to realize the complementary information of infrared and visible light multiband information. The experimental results show that it has a more obvious advantage in detection accuracy than the single-band target detection model while the decision-level fusion target detection model meets the real-time requirements and also verifies the effectiveness of the above algorithm.

Decision-level fusion of SAR and IR sensor information for automatic target detection

2017 ◽  
Author(s):  
Young-Rae Cho ◽  
Sung-Hyuk Yim ◽  
Hyun-Woong Cho ◽  
Jin-Ju Won ◽  
Woo-Jin Song ◽  
...  
Keyword(s):  
Target Detection ◽  
Automatic Target Detection ◽  
Decision Level ◽  
Ir Sensor ◽  
Sensor Information ◽  
Decision Level Fusion ◽  
Level Fusion

scholarly journals Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning

2021 ◽  
Vol 13 (22) ◽  
pp. 4668
Author(s):  
Stella Ofori-Ampofo ◽  
Charlotte Pelletier ◽  
Stefan Lang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Decision Level ◽  
Type Mapping ◽  
Learning Techniques ◽  
Crop Type ◽  
Single Sensor ◽  
Decision Level Fusion ◽  
Level Fusion ◽  
Sentinel 2

Crop maps are key inputs for crop inventory production and yield estimation and can inform the implementation of effective farm management practices. Producing these maps at detailed scales requires exhaustive field surveys that can be laborious, time-consuming, and expensive to replicate. With a growing archive of remote sensing data, there are enormous opportunities to exploit dense satellite image time series (SITS), temporal sequences of images over the same area. Generally, crop type mapping relies on single-sensor inputs and is solved with the help of traditional learning algorithms such as random forests or support vector machines. Nowadays, deep learning techniques have brought significant improvements by leveraging information in both spatial and temporal dimensions, which are relevant in crop studies. The concurrent availability of Sentinel-1 (synthetic aperture radar) and Sentinel-2 (optical) data offers a great opportunity to utilize them jointly; however, optimizing their synergy has been understudied with deep learning techniques. In this work, we analyze and compare three fusion strategies (input, layer, and decision levels) to identify the best strategy that optimizes optical-radar classification performance. They are applied to a recent architecture, notably, the pixel-set encoder–temporal attention encoder (PSE-TAE) developed specifically for object-based classification of SITS and based on self-attention mechanisms. Experiments are carried out in Brittany, in the northwest of France, with Sentinel-1 and Sentinel-2 time series. Input and layer-level fusion competitively achieved the best overall F-score surpassing decision-level fusion by 2%. On a per-class basis, decision-level fusion increased the accuracy of dominant classes, whereas layer-level fusion improves up to 13% for minority classes. Against single-sensor baseline, multi-sensor fusion strategies identified crop types more accurately: for example, input-level outperformed Sentinel-2 and Sentinel-1 by 3% and 9% in F-score, respectively. We have also conducted experiments that showed the importance of fusion for early time series classification and under high cloud cover condition.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

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