Abstract
Deep learning has brought revolutionary progress to computer vision, so intelligent inspection equipment based on computer vision has developed rapidly. However, due to the large number of existing deep features, it is difficult to deploy it on mobile devices to achieve real-time tracking speed. This paper presents a target-aware deep feature compression for power intelligent inspection tracking. First, a negative balance loss function is designed to mine channel features suitable for the current inspection scene by shrinking the contribution of pure background negative samples and enhancing the impact of difficult negative samples. Based on this, the deep feature compression model is combined with Siamese tracking framework to achieve real-time and robust tracking. Finally, we evaluate the proposed method on real application scenarios and general data to prove the practicability of the proposed method.
Highlights
A stable and reliable power system is the key to ensuring people’s livelihood and economic development [1, 2]
We compared it with two classical methods, including the tracking methods kernelized correlation filters (KCF), circulant structure of tracking-by-detection with kernels (CSK), and ECO, based on correlation filter, and the tracking methods target-aware deep tracking (TADT) and high performance visual tracking with Siamese region proposal network (SiamRPN) [42] based on Siamese framework
In the intelligent inspection of power system based on mobile robot, the task of mobile robot is to read the indicator number of instrument panel using visual inspection technology. e whole task is divided into three parts. (1) e first part is to use detection technology to identify the instrument that needs to be read. (2) e second part is to use tracking technology to continuously track the instrument until the instrument picture is clear. (3) e third part is using segmentation method to read the indicator number of instrument. e tracking method proposed by us is mainly used for instrument continuous tracking in the second part
Summary
A stable and reliable power system is the key to ensuring people’s livelihood and economic development [1, 2]. Many lightweight methods for deep networks have been proposed, mainly including five methods: (1) parameter pruning, (2) parameter sharing, (3) low-rank decomposition, (4) designing compact convolutional filters, and (5) knowledge distillation. Under the guidance of these two losses, only the target information given in the first frame can be utilized to effectively eliminate redundant channel features and achieve robust and high-speed tracking. It is observed that TADT uses a large number of negative samples of pure background in regression loss learning to occupy more contributions, which leads to the activated channel features paying more attention to the difference between the target and the pure background, while tracking requires more attention to the interference that is indistinguishable from the target. Inspired by the previously mentioned work, we proposed a target perception deep feature compression method for intelligent detection target tracking. The contribution of hard negative samples was enhanced, so that the activated channel features were more focused on the difference between target and similar interference. en, the compressed feature is combined with Siamese framework to achieve robust and realtime tracking
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