scholarly journals A Robust Thermal Infrared Vehicle and Pedestrian Detection Method in Complex Scenes

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1240
Author(s):  
Yang Liu ◽  
Hailong Su ◽  
Cao Zeng ◽  
Xiaoli Li
Keyword(s):  
Detection Method ◽  
Selection Process ◽  
Pedestrian Detection ◽  
Thermal Infrared ◽  
Single Shot ◽  
Detection Algorithms ◽  
Complex Scenes ◽  
Thermal Infrared Images ◽  
Online Feature Selection ◽  
Huge Challenge

In complex scenes, it is a huge challenge to accurately detect motion-blurred, tiny, and dense objects in the thermal infrared images. To solve this problem, robust thermal infrared vehicle and pedestrian detection method is proposed in this paper. An important weight parameter β is first proposed to reconstruct the loss function of the feature selective anchor-free (FSAF) module in its online feature selection process, and the FSAF module is optimized to enhance the detection performance of motion-blurred objects. The proposal of parameter β provides an effective solution to the challenge of motion-blurred object detection. Then, the optimized anchor-free branches of the FSAF module are plugged into the YOLOv3 single-shot detector and work jointly with the anchor-based branches of the YOLOv3 detector in both training and inference, which efficiently improves the detection precision of the detector for tiny and dense objects. Experimental results show that the method proposed is superior to other typical thermal infrared vehicle and pedestrian detection algorithms due to 72.2% mean average precision (mAP).

scholarly journals A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

2021 ◽  
Vol 13 (9) ◽  
pp. 1619
Author(s):  
Bin Yan ◽  
Pan Fan ◽  
Xiaoyan Lei ◽  
Zhijie Liu ◽  
Fuzeng Yang
Keyword(s):  
Real Time ◽  
Apple Tree ◽  
Detection Method ◽  
Recognition Algorithm ◽  
Medium Size ◽  
Feature Maps ◽  
Detection Algorithms ◽  
Fusion Mode ◽  
Visual Attention Mechanism ◽  
Tree Image

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

scholarly journals Transformations in the Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5061
Author(s):  
Adam Dlesk ◽  
Karel Vach ◽  
Karel Pavelka
Keyword(s):  
Structure From Motion ◽  
Thermal Infrared ◽  
Processing Method ◽  
Spatial Transformation ◽  
Infrared Images ◽  
Low Resolution ◽  
Thermal Infrared Images ◽  
Rgb Images ◽  
Motion Method

The photogrammetric processing of thermal infrared (TIR) images deals with several difficulties. TIR images ordinarily have low-resolution and the contrast of the images is very low. These factors strongly complicate the photogrammetric processing, especially when a modern structure from motion method is used. These factors can be avoided by a certain co-processing method of TIR and RGB images. Two of the solutions of co-processing were suggested by the authors and are presented in this article. Each solution requires a different type of transformation–plane transformation and spatial transformation. Both types of transformations are discussed in this paper. On the experiments that were performed, there are presented requirements, advantages, disadvantages, and results of the transformations. Both methods are evaluated mainly in terms of accuracy. The transformations are presented on suggested methods, but they can be easily applied to different kinds of methods of co-processing of TIR and RGB images.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

scholarly journals Applications of Satellite Thermal Infrared Images for Monitoring North Water during the Periods of Polar Darkness

1980 ◽  
Vol 25 (93) ◽  
pp. 425-438
Author(s):  
B. Dey
Keyword(s):  
Satellite Images ◽  
Open Water ◽  
Thermal Infrared ◽  
Water Area ◽  
Infrared Images ◽  
Baffin Bay ◽  
The North ◽  
Thermal Infrared Images ◽  
North Water

AbstractThe study reported here illustrates the unique value of NOAA thermal infrared (TIR) images for monitoring the North Water area in Smith Sound and northern Baffin Bay during the periods of polar darkness. Wintertime satellite images reveal that, during the months of December through February, open water and thin ice occur in a few leads and polynyas. However, in March, the areas of open water and thin ice decrease to a minimum with a consequent higher concentration of ice. Two ice dams, in northern Kennedy Channel and in northern Smith Sound, regulate the flow of ice into northern Baffin Bay and also determine the areal variations of open water and thin ice in Smith Sound.

A Novel Fast Pedestrian Detection Method

2015 ◽  
Vol 738-739 ◽  
pp. 538-541
Author(s):  
Fu Qiang Zhou ◽  
Yan Li
Keyword(s):  
Video Streaming ◽  
Local Binary Pattern ◽  
Detection Method ◽  
Pedestrian Detection ◽  
Original Method ◽  
Histograms Of Oriented Gradients ◽  
Detection Approach

This paper presents novel pedestrian detection approach in video streaming, which could process frames rapidly. The method is based on cascades of HOG-LBP (Histograms of Oriented Gradients-Local Binary Pattern), but combines non-negative factorization to reduce the length of the feature, aiming at realizing a more efficient way of detection, remedying the slowness of the original method. Experiments show our method can process faster than HOG and HOG-LBP, and more accurate than HOG, which has better performance in pedestrian detection in video streaming.

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