In many scene classification applications, the variety of surface objects, high within-category diversity and between-category similarity carry challenges for the classification Framework. Most of CNN-based classification methods only extract image features from a single network layer, which may cause the completed image information difficult to extract in complex scenes. We propose a novel transfer deep combined convolutional activations (TDCCA) to integrate both the low-level and high-level features. Extensive comparative experiments are conducted on UC Merced database, Aerial Image database and NWPU-RESISC45 database. The results reveal that our proposed TDCCA achieves higher experimental accuracies than other up-to-date popular methods.
How to detect targets under poor imaging conditions is receiving significant attention in recent years. The accuracy of object recognition position and recall rate may decrease for the classical YOLO model under poor imaging conditions because targets and their backgrounds are hard to discriminate. We proposed the improved YOLOv3 model whose basic structure of the detector is based on darknet-53, which is an accurate but efficient network for image feature extraction. Then Squeeze-and-Excitation (SE) structure is integrated after non-linearity of convolution to collect spatial and channel-wise information within local receptive fields. To accelerate inference speed, Nvidia TenorRT 6.0 is deployed into on Nvidia Jetson series low power platform. Experiments results show that the improved model may greatly achieve the inference speed without significantly reducing the detection accuracy comparing with the classic YOLOv3 model and some other up-to-date popular methods.
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