Detecting objects in aerial images is a long-standing and challenging problem since the objects in aerial images vary dramatically in size and orientation. Most existing neural network based methods are not robust enough to provide accurate oriented object detection results in aerial images since they do not consider the correlations between different levels and scales of features. In this paper, we propose a novel two-stage network-based detector with a daptive f eature f usion towards highly accurate oriented object det ection in aerial images, named AFF-Det. First, a multi-scale feature fusion module (MSFF) is built on the top layer of the extracted feature pyramids to mitigate the semantic information loss in the small-scale features. We also propose a cascaded oriented bounding box regression method to transform the horizontal proposals into oriented ones. Then the transformed proposals are assigned to all feature pyramid network (FPN) levels and aggregated by the weighted RoI feature aggregation (WRFA) module. The above modules can adaptively enhance the feature representations in different stages of the network based on the attention mechanism. Finally, a rotated decoupled-RCNN head is introduced to obtain the classification and localization results. Extensive experiments are conducted on the DOTA and HRSC2016 datasets to demonstrate the advantages of our proposed AFF-Det. The best detection results can achieve 80.73% mAP and 90.48% mAP respectively on these two datasets, outperforming recent state-of-the-art methods.

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The whole ecosystem is suffering from serious plastic pollution. Automatic and accurate classification is an essential process in plastic effective recycle. In this work, we proposed an accurate approach for plastics classification using a residual network based on laser-induced breakdown spectroscopy (LIBS). To increasing efficiency, the LIBS spectral data were compressed by peak searching algorithm based on continuous wavelet, then were transformed to characteristic images for training and validation of the residual network. Acrylonitrile butadiene styrene (ABS), polyamide (PA), polymethyl methacrylate (PMMA), and polyvinyl chloride (PVC) from 13 manufacturers were used. The accuracy of the proposed method in few-shot learning was evaluated. The results show that when the number of training image data was 1, the verification accuracy of classification by plastic type under residual network still kept 100%, which was much higher than conventional classification algorithms (BP, kNN and SVM). Furthermore, the training and testing data were separated from different manufacturers to evaluate the anti-interference properties of the proposed method from various additives in plastics, where 73.34% accuracy was obtained. To demonstrate the superiority of classification accuracy in the proposed method, all the evaluations were also implemented by using conventional classification algorithm (kNN, BP, SVM algorithm). The results confirmed that the residual network has a significantly higher accuracy in plastics classification and shows great potential in plastic recycle industries for pollution mitigation.

Fast elemental analyses is essential in aluminum alloy manufacture. In this work, fast determination of copper, magnesium, and manganese elements in aluminum alloy using laser-induced breakdown spectroscopy based on fiber...

Fiber lasers have great advantages in long-time operation and stable power, showing great potentials in laser-induced breakdown spectroscopy (LIBS) for industrial online diagnosis. However, too high repetition frequency of fiber...