A Novel Region Selection Algorithm for Auto-focusing Method Based on Depth from Focus

Chen, Jian; Chen, D. Q.; Meng, Sheng-Hui

doi:10.1007/978-3-319-68527-4_11

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…By the simple structure of the dilated filters, they are also a good choice when the runtime matters. The other classical filter extensions from [33,[35][36][37]40] require a larger number of operations in order to return the resulting edge pixels, whereas the custom dilated filters always have the same number of operations for any extension. It seems that the gaps imply a speed up.…”

Section: Discussionmentioning

confidence: 99%

“…First-order derivative orthogonal gradient operators are the most basic operators and have been extensively researched over the decades. We consider in our analysis the following edge-detected operators and their extensions: pixel difference operator [32], separated pixel difference operator [32], Sobel operator and the extension to a 5 × 5 or 7 × 7 kernel [33][34][35][36][37], Prewitt operator and the extension to a 5 × 5 or 7 × 7 kernel [34,36], Kirsch operator [38] and the 5 × 5 kernel expansion [33], Kitchen and Malin operator [39], Kayalli operator, Scharr operator and the extensions to 5 × 5 kernel [36,40], Kroon operator [41] and Orhei operator [42].…”

Section: First-order Derivative Orthogonal Gradient Operatorsmentioning

confidence: 99%

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Dilated Filters for Edge-Detection Algorithms

et al. 2021

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Edges are a basic and fundamental feature in image processing that is used directly or indirectly in huge number of applications. Inspired by the expansion of image resolution and processing power, dilated-convolution techniques appeared. Dilated convolutions have impressive results in machine learning, so naturally we discuss the idea of dilating the standard filters from several edge-detection algorithms. In this work, we investigated the research hypothesis that use dilated filters, rather than the extended or classical ones, and obtained better edge map results. To demonstrate this hypothesis, we compared the results of the edge-detection algorithms using the proposed dilation filters with original filters or custom variants. Experimental results confirm our statement that the dilation of filters have a positive impact for edge-detection algorithms from simple to rather complex algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: First-order Derivative Orthogonal Gradient Operatorsmentioning

confidence: 99%

Dilated Filters for Edge-Detection Algorithms

et al. 2021

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“…where j d the position information of the j-th detection frame is, i y is the position prediction information of the i-th tracker by Kalman filter, and i S is the covariance matrix between the detection and tracking positions. When the calculated Mahalanobis distance is less than the set threshold, the association is considered successful, as shown in Equation (14), where χ is the indicator function and (1) t is the specified threshold.…”

Section: Objects Tracking Methodsmentioning

confidence: 99%

“…There are many traditional detection methods proposed by researchers. These algorithms have three common processes, including region selection [1], feature extraction [2] and classification [3]. Region selection generally uses a sliding window method to traverse the image globally.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT

2021

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Ship detection and tracking is an important task in video surveillance in inland waterways. However, ships in inland navigation are faced with accidents such as collisions. For collision avoidance, we should strengthen the monitoring of navigation and the robustness of the entire system. Hence, this paper presents ship detection and tracking of ships using the improved You Only Look Once version 3 (YOLOv3) detection algorithm and Deep Simple Online and Real-time Tracking (Deep SORT) tracking algorithm. Three improvements are made to the YOLOv3 target detection algorithm. Firstly, the Kmeans clustering algorithm is used to optimize the initial value of the anchor frame to make it more suitable for ship application scenarios. Secondly, the output classifier is modified to a single Softmax classifier to suit our ship dataset which has three ship categories and mutual exclusion. Finally, Soft Non-Maximum Suppression (Soft-NMS) is introduced to solve the deficiencies of the Non-Maximum Suppression (NMS) algorithm when screening candidate frames. Results showed the mean Average Precision (mAP) and Frame Per Second (FPS) of the improved algorithm are increased by about 5% and 2, respectively, compared with the existing YOLOv3 detecting Algorithm. Then the improved YOLOv3 is applied in Deep Sort and the performance result of Deep Sort showed that, it has greater performance in complex scenes, and is robust to interference such as occlusion and camera movement, compared to state of art algorithms such as KCF, MIL, MOSSE, TLD, and Median Flow. With this improvement, it will help in the safety of inland navigation and protection from collisions and accidents.

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“…ere are many traditional object detection models proposed by researchers. Traditional object detection models mainly rely on region selection [6], feature extraction [7], and classifier classification [8]. In 2006, Dalal and Triggs proposed the HOG algorithm [9], which composes features by calculating and counting the histogram of the local area's gradient direction.…”

Section: Introductionmentioning

confidence: 99%

ShipYOLO: An Enhanced Model for Ship Detection

Han

Zhao

Ning

et al. 2021

Journal of Advanced Transportation

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The application of ship detection for assistant intelligent ship navigation has stringent requirements for the model’s detection speed and accuracy. In response to this problem, this study uses an improved YOLO-V4 detection model (ShipYOLO) to detect ships. Compared to YOLO-V4, the model has three main improvements. Firstly, the backbone network (CSPDarknet) of YOLO-V4 is optimized. In the training process, the 3 × 3 convolution, 1 × 1 convolution, and identity parallel mode are used to replace the original feature extraction component (ResUnit) and more features are extracted. In the inference process, the branch parameters are combined to form a new backbone network named RCSPDarknet, which improves the inference speed of the model while improving the accuracy. Secondly, in order to solve the problem of missed detection of the small-scale ships, we designed a new amplified receptive field module named DSPP with dilated convolution and Max-Pooling, which improves the model’s acquisition of small-scale ship spatial information and robustness of ship target space displacement. Finally, we use the attention mechanism and Resnet’s shortcut idea to improve the feature pyramid structure (PAFPN) of YOLO-V4 and get a new feature pyramid structure named AtFPN. The structure effectively improves the model’s feature extraction effect for ships of different scales and reduces the number of model parameters, further improving the model’s inference speed and detection accuracy. In addition, we have created a ship dataset with a total of 2238 images, which is a single-category dataset. The experimental results show that ShipYOLO has the advantage of faster speed and higher accuracy even in different input sizes. Considering the input size of 320 × 320 on the PC equipped with NVIDIA 1080Ti GPU, the FPS and mAP@5 : 5:95 (mAP90) of ShipYOLO are increased by 23.7% and 13.6% (10.6%), respectively, with an input size of 320 × 320, ShipYOLO, compared to YOLO-V4.

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A Novel Region Selection Algorithm for Auto-focusing Method Based on Depth from Focus

Cited by 4 publications

References 5 publications

Dilated Filters for Edge-Detection Algorithms

Dilated Filters for Edge-Detection Algorithms

Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT

ShipYOLO: An Enhanced Model for Ship Detection

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