The current target tracking and detection algorithms often have mistakes and omissions when the target is occluded or small. To overcome the defects, this paper integrates bi-directional feature pyramid network (BiFPN) into cascade region-based convolutional neural network (R-CNN) for live object tracking and detection. Specifically, the BiFPN structure was utilized to connect between scales and fuse weighted features more efficiently, thereby enhancing the network’s feature extraction ability, and improving the detection effect on occluded and small targets. The proposed method, i.e., Cascade R-CNN fused with BiFPN, was compared with target detection algorithms like Cascade R-CNN and single shot detection (SSD) on a video frame dataset of wild animals. Our method achieved a mean average precision (mAP) of 91%, higher than that of SSD and Cascade R-CNN. Besides, it only took 0.42s for our method to detect each image, i.e., the real-time detection was realized. Experimental results prove that the proposed live object tracking and detection model, i.e., Cascade R-CNN fused with BiFPN, can adapt well to the complex detection environment, and achieve an excellent detection effect.
This paper presents a novel approach to detecting worms based on particle filter. The approach collects data through honeynet and uses CUSUM to detect the abnormal changes of counts of packet source address in a t sampling. If the change rate exceeds a certain threshold, it will activate particle filter to estimate its growth rate in order to confirm the existence of worms. The experimental results show that the approach can detect unknown worms quickly and contain the large-scale spread of worms if it combines with the intrusion detection system and firewall.
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