scholarly journals Lesion-wise evaluation for effective performance monitoring of small object segmentation

2021 ◽  
Author(s):  
Irme Groothuis ◽  
Carole Sudre ◽  
Silvia Ingala ◽  
Josephine Barnes ◽  
Juan Domingo Gispert Lopez ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Object Segmentation ◽  
Small Object ◽  
Effective Performance

Small object segmentation with fully convolutional network based on overlapping domain decomposition

2019 ◽  
Vol 30 (4) ◽  
pp. 707-716 ◽  
Author(s):  
Jinhee Park ◽  
Dokyeong Kwon ◽  
Bo Won Choi ◽  
Ga Young Kim ◽  
Kwang Yong Kim ◽  
...  
Keyword(s):  
Domain Decomposition ◽  
Object Segmentation ◽  
Small Object ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Overlapping Domain Decomposition

scholarly journals A Guide to Evaluating Marine Spatial Plans

2017 ◽  
Author(s):  
Charles Ehler
Keyword(s):  
Performance Monitoring ◽  
Monitoring And Evaluation ◽  
Know How ◽  
Marine Management ◽  
Management Objectives ◽  
Effective Performance ◽  
Management Plans ◽  
Marine Areas ◽  
Expected Outcomes

This guide on performance monitoring and evaluation (evaluation) is intended for practitioners responsible for planning and managing marine areas. Practitioners are the managers and stakeholders who are responsible for designing, planning, implementing, monitoring, and evaluating marine management plans. While its focus is on the performance monitoring and evaluation of MSP, planners and managers should know how to incorporate monitoring and evaluation considerations into the MSP process from its very beginning, and not wait until a plan is completed before thinking about how to measure “success”. Effective performance monitoring and evaluation is only possible when management objectives and expected outcomes are written in a way that is measurable, either quantitatively or qualitatively.

AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

2021 ◽  
Vol 13 (4) ◽  
pp. 555
Author(s):  
Ikhwan Song ◽  
Sungho Kim
Keyword(s):  
Object Segmentation ◽  
State Of The Art ◽  
Threshold Value ◽  
Small Object ◽  
Infrared Images ◽  
Average Precision ◽  
Trade Off ◽  
Good Evaluation ◽  
Single Dataset ◽  
Evaluation Metric

Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications, such as surveillance, tracking, and many fields related to infrared searching and tracking. F1-score may be a good evaluation metric for this problem. However, since the F1-score only depends upon a specific threshold value, it cannot reflect the user’s requirements according to the various application environment. Therefore, several metrics are commonly used together. Now we introduce F-area, a novel metric for a panoptic evaluation of average precision and F1-score. It can simultaneously consider the performance in terms of real application and the potential capability of a model. Furthermore, we propose a new network, called the Amorphous Variable Inter-located Network (AVILNet), which is of pliable structure based on GridNet, and it is also an ensemble network consisting of the main and its sub-network. Compared with the state-of-the-art ISOS methods, our model achieved an AP of 51.69%, F1-score of 63.03%, and F-area of 32.58% on the International Conference on Computer Vision 2019 ISOS Single dataset by using one generator. In addition, an AP of 53.6%, an F1-score of 60.99%, and F-area of 32.69% by using dual generators, with beating the existing best record (AP, 51.42%; F1-score, 57.04%; and F-area, 29.33%).

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