Small target recognition method on weak features

Wu, QingE; Ziming, An; Chen, Hu; Qian, Xiaoliang; Sun, Lijun

doi:10.1007/s11042-020-09926-y

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…Kong et al [6] proposed an efficient feature extraction method, which effectively improves the real-time performance of the algorithm. For small targets in complex environments that are easily masked by other objects or noise, Wu et al [7] proposed an open-closed transformation algorithm to eliminate or weaken the background and noise. This algorithm extracts the weakened features by eliminating noise to achieve the recognition of small targets, which effectively improves the recognition efficiency of small targets.…”

Section: Related Workmentioning

confidence: 99%

EHDC: enhanced dilated convolution framework for underwater blurred target recognition

Cai

Qin

2022

Robotica

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The autonomous underwater vehicle (AUV) has a problem with feature loss when recognizing small targets underwater. At present, algorithms usually use multi-scale feature extraction to solve the problem, but this method increases the computational effort of the algorithm. In addition, low underwater light and turbid water result in incomplete information on target features. This paper proposes an enhanced dilated convolution framework (EHDC) for underwater blurred target recognition. Firstly, this paper extracts small target features through hybrid dilated convolution networks, increasing the perceptive field of the algorithm without increasing the computational power of the algorithm. Secondly, the proposed algorithm learns spatial semantic features through an adaptive correlation matrix and compensates for the missing features of the target. Finally, this paper fuses spatial semantic features and visual features for the recognition of small underwater blurred targets. Experiments show that the proposed method improves the recognition accuracy by 1.04% compared to existing methods when recognizing small underwater blurred targets.

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Section: Related Workmentioning

confidence: 99%