Reweighting neural network examples for robust object detection at sea

Abstract

Deep neural networks have had profound significance in addressing visual object detection and classification tasks. However, though with the caveat of needing large amounts of annotated training data. Furthermore, the possibility of neural networks overfitting to the biases and faults included in their respective datasets. In this work, methods for achieving robust neural networks, able to tolerate untrusted and possibly erroneous training data, are explored. The proposed method is shown to improve performance and help neural networks learn from untrusted data, provided a thoroughly annotated subset.

Dynamic attention network for flame detection

“…Li et al. achieve fast and advanced fire detection by using famous detectors, that is, faster RCNN [14], YOLO [15], SSD [16], RFCN [17], to remedy the low accuracy of conventional methods [9]. Zhen et al.…”

“…With the development of deep learning [2–6], frameworks applying deep convolutional neural networks (CNNs) can detect fires more accurately and efficiently [7–9, 10–13]. Muhammad et al.…”

“…Muhammad et al propose a computationally efficient CNN structure to localize, and understand the semantic of fire scenes [8]. Li et al achieve fast and advanced fire detection by using famous detectors, that is, faster RCNN [14], YOLO [15], SSD [16], RFCN [17], to remedy the low accuracy of conventional methods [9]. Zhen et al design a novel flame detection method with a candidate target area extraction strategy which reaches higher recall rate on the homemade database [10].…”

“…With the development of deep learning [2][3][4][5][6], frameworks applying deep convolutional neural networks (CNNs) can detect fires more accurately and efficiently [7][8][9][10][11][12][13]. Muhammad et al propose a computationally efficient CNN structure to localize, and understand the semantic of fire scenes [8].…”

Buoy Light Pattern Classification for Autonomous Ship Navigation Using Recurrent Neural Networks

“…Whether a vessel is manned or unmanned, situational awareness is crucial for safe navigation [2], [3], and significant efforts have been reported on daytime awareness. Perception and understanding at daytime have included classification and tracking of objects, such as ships and buoys, are active topics of research [4], [5], [6], and robust classification from weather-degraded [7] or generally poorly annotated image data [8] is a challenge. At night-time, the same task becomes different and more demanding.…”