Real-time scene parsing through object detection running on an embedded device is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, we redesign a lightweight network without notably reducing detection accuracy. Based on the Darknet-53, we use depth separable convolutions and pointwise group convolutions to reduce the parameter size of the network. A feature extraction backbone network with a parameter size of only 16 percent of darknet-53 is constructed. Meanwhile, in order to compensate for the degradation of accuracy, we have added a Multi-Scale Feature Pyramid Network based on a simple U-shaped structure to improve the performance of multi-scale object detection, which called it Mini-YOLOv3. It has smaller model size and fewer trainable parameters and floating point operations (FLOPs) in comparison of YOLOv3. We evaluate Mini-YOLOv3 on MS-COCO benchmark dataset; The parameter size of Mini-YOLOv3 is only 23% of YOLOv3 and achieves comparable detection accuracy as YOLOv3 but only requires 1/2 detect time, Specifically, Mini-YOLOv3 achieves mAP-50 of 52.1 at speed of 67 fps. INDEX TERMS Real-time object detector, embedded applications, convolutional neural network (CNN). YOLOv3.
Deep learning achieves substantial improvements in face detection. However, the existing methods need to input fixed-size images for image processing and most methods use a single network for feature extraction, which makes the model generalization ability weak. In response to the above problems, our framework leverages a cascaded architecture with three stages of deep convolutional networks to improve detection performance. The network can predict face in a coarse-to-fine manner. We replace the standard convolution with a combination of separable convolution and residual structure in the network. Extensive experiments on the challenging FDDB and WIDER FACE benchmarks demonstrate that our method achieves competitive accuracy to the state-of-the-art techniques while keeps real-time performance. INDEX TERMS Face detection, cascade convolutional neural networks, depthwise separable convolution, residual structure.
At present, there are two main problems with fruit-and vegetable-picking robots. One is that complex scenes (with backlighting, direct sunlight, overlapping fruit and branches, blocking leaves, etc.) obviously interfere with the detection of fruits and vegetables; the other is that an embedded platform needs a lighter detection method due to performance constraints. To address these problems, a fast tomato detection method based on improved YOLOv3-tiny is proposed. First, we improve the precision of the model by improving the backbone network; second, we use image enhancement to improve the detection ability of the algorithm in complex scenes. Finally, we design several groups of comparative experiments to prove the rationality and feasibility of this method. The experimental results show that the f1-score of the tomato recognition model proposed in this paper is 91.92%, which is 12% higher than that of YOLOv3-tiny; the detection speed on a CPU can reach 25 frames/s, and the inferential speed is 40.35 ms, equivalent to that of YOLOv3-tiny. Through comparative experiments, we can see that the comprehensive performance of our method is better than that of other state-of-the-art methods.INDEX TERMS Real-time object detection, deep learning, picking robot, tomato, embedded device.
Aiming at the situation that complementary ensemble empirical mode decomposition (CEEMD) noise suppression method may produce redundant noise and wavelet transform easily loses high-frequency detail information, considering wavelet packet transform can be used to perform better time-frequency localization analysis on signals containing a large amount of medium and high frequency information, according to the noise and useful signal components of both the characteristic of self-correlation function is different, the CEEMD and wavelet packet threshold jointed method is proposed. The method uses the energy concentration ratio to find noise and useful signal component demarcation point to denoise the microseismic signals. Firstly, we utilize adaptively decompose the signal from high frequency to low frequency by the CEEMD; Secondly, using the self-correlation method to select the intrinsic mode function (IMF) that needs noise suppression, the wavelet suppression method is used to suppress the noise of several high-frequency components whose self-correlation coefficient is below the critical value K; Finally, the IMF component after the wavelet packet threshold noise suppression is reconstructed with the noisefree IMF component. In order to verify the effectiveness of the proposed method on the noise suppression of microseismic signal, we added a Gaussian white noise to the Ricker wavelet signal similar to the microseismic signal. The experimental results show that the signal-to-noise ratio (SNR) of the signal is raised more than 10dB. The energy percentage is higher than 92%. In practical engineering, our proposal achieves an effective noise suppression effect on the microseismic signal.INDEX TERMS Complementary ensemble empirical mode decomposition, wavelet packet threshold, selfcorrelation, noise suppression.
The traditional edge detection-based shoreline extraction method is severely disturbed by noise, and it is difficult to obtain a continuous coastline. In response to the above problems, we propose a coastline extraction method based on convolutional neural networks. Firstly, we replace the standard convolution with the Mini-Inception structure in the backbone network to extract multi-scale features of the object, and all the multi-scale features are concatenated. Then, we use the leaky-ReLU activation function instead of the ReLU activation function to avoid the problem that ''dead'' neurons cannot learn the effective features of remote sensing images. Finally, the network fully exploits multi-level information of objects to perform the image-to-image prediction. We carried out experiments on the remote sensing images of Jiaozhou Bay in Qingdao. The experimental results showed that our method could effectively extract the coastline automatically, and the producer's accuracy and the user's accuracy were higher than the comparison methods. INDEX TERMS Coastline extraction, remote sensing images, convolutional neural networks, backbone networks, activation function.
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