In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.
Highlights
A series of strategies including feature concatenation, tweaks of ResNet50, and modification of the default anchors with the chaos optimization-based
k
-means algorithm were proposed to improve the detection performance of the original Faster R-CNN.
The improved Faster R-CNN achieved an average precision of 97.71%, which is 5.98% higher than that of the original Faster R-CNN and 14.38% higher than that of YOLOv2.
The improved Faster R-CNN greatly boosted the detection performance for potato buds without incurring any noticeable additional computational overhead.
Abstract.
This article proposes an improved Faster R-CNN model to achieve better detection performance for potato buds, with the goal of preparing for the automated cutting of seed potatoes. Detection results of Faster R-CNNs with eight pretrained networks were compared, and ResNet50 was adopted as the backbone network in Faster R-CNN. On this basis, three model strategies, including feature concatenation, tweaks of ResNet50, and modification of the default anchors with the chaos optimization-based k-means algorithm, were proposed to improve the detection performance for potato buds. Experimental results on the test set demonstrated that the improved Faster R-CNN achieved an average precision (AP) of 97.71%, which is 5.98% higher than that of the original Faster R-CNN and 14.38% higher than that of YOLOv2. In addition, the average running time per image with the improved Faster R-CNN was 0.166 s, the same as that of the original Faster R-CNN. In other words, the improved Faster R-CNN greatly boosted the detection performance for potato buds without incurring any noticeable additional computational overhead, thus satisfying the requirements for real-time processing. Consequently, the improved Faster R-CNN can provide a solid foundation for the automated cutting of seed potatoes. Keywords: Chaos optimization-based k-means, Default anchors, Faster R-CNN, Feature concatenation, Potato bud detection, Tweaks of ResNet50.
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