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1.
Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset. 相似文献
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Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation. 相似文献
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《中国有色金属学会会刊》2021,31(8):2468-2478
An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control. 相似文献
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《International Journal of Hydrogen Energy》2022,47(19):10747-10761
To obtain the mixing enhancement mechanism of H2–Water combined jets in supersonic crossflows in a combustor with expanded section for rotating detonation ramjet, the flow field shape and spray structure were studied by experimental and numerical methods. The Eulerian–Lagrangian method was used to investigate the diffusion mechanism and H2–Water interaction law of combined jets with different sequences. At the same time, high-speed photography and the schlieren technique were used to capture the flow field. The effects of jet pressure drop, orifice diameter, orifice spacing, incoming Mach number, and other parameters on the penetration depth of water jets were studied. The results of experiment and simulation show that using H2–Water combined jets, the penetration depth of the jet spray can be greatly increased and the jet mixing effect can be significantly improved, which will contribute to the engine's ignition and stable combustion. In the case of pre-water/post-H2, the penetration depth of the hydrogen jet is greater. In the case of pre-H2/post-water, the hydrogen jet raises the water spray mainly by protecting the integrity of the water column. 相似文献
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为研究钢管套筒灌浆连接轴向受拉破坏过程及破坏机理,试验中设计了16组48个钢管套筒灌浆连接试件,试件采用钢板代替圆钢管,并进行静载试验。分析了灌浆料裂缝扩展过程、荷载-相对位移曲线,并对抗剪键高距比、灌浆料厚度、侧向力等因素对破坏过程及承载力的影响进行分析。结果表明:对于不设置抗剪键的套筒灌浆连接试件,斜裂缝随机产生,裂缝分布不均匀;对于设置抗剪键的套筒灌浆连接试件,裂缝首先出现在底部抗剪键位置处,与水平方向夹角约为30°,随后在中部和上部抗剪键位置处分别出现斜裂缝。由于每个抗剪键上荷载分担并不均匀,与抗剪键接触的灌浆料逐渐达到极限压应力,达到极限状态时,承载力全部由抗剪键间的机械咬合力承担,在连接承载力中,可忽略摩擦力和胶结力作用。随着抗剪键高距比h/s增大,各试件初始剪切刚度相差不大,承载力增大,但增幅逐渐减小,建议抗剪键高距比0.06g/s>0.3,同时需要满足灌浆料灌注的施工要求。 相似文献
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