High-Efficiency Progressive Transmission and Automatic Recognition of Wildlife Monitoring Images With WISNs

Abstract

Wireless image sensor networks (WISNs) are widely applied in wildlife monitoring, as they present a better performance in remote, real-time monitoring. However, traditional WISNs suffer from the limitations of low processing capability, power consumption restrictions and narrow transmission bandwidth. For the contradiction between the above limitations of WISNs and the wildlife monitoring images with high resolution and complex background, we propose a novel wildlife intelligent monitoring system. On the foundation of saliency object detection, the convolutional encoder-decoder network is utilized to realize the progressive compression transmission and restoration for wildlife monitoring images, which guarantees the transmission efficiency and quality of wildlife part. Moreover, to deal with the problems of high labor intensity, low efficiency and low recognition accuracy in classical manual sorting method, an improved Faster RCNN algorithm is proposed on the automatic recognition of wildlife images. The experimental results on our own wildlife dataset, show that the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) are improved by 7.93%, 18.15% and 7.01%, 12.67% respectively on reconstruction image, when compared with the set partitioned in hierarchical tree (SPIHT) and embedded zerotree (EZW) algorithms. Compared with the traditional Faster RCNN algorithm, the recognition accuracy of six species wildlife is respectively improved by 1%, 18%, 5%, 17%, 2% and 19%, and the final mAP value reaches to 92.2% in test set increased by 10.9%, which demonstrates the proposed algorithm can ideally achieve the wildlife intelligent monitoring with WISNs.

A novel efficient wildlife detecting method with lightweight deployment on UAVs based on YOLOv7

“…Roy et al [28] addressed the aforementioned challenges by integrating DenseNet modules into YOLOv4 to improve critical feature information and designing two residual blocks on the backbone of CSPDarknet53 for more distinctive deep spatial feature extraction. Feng et al [29] developed a novel wildlife detection system based on wireless image sensor networks (WISNs) and employed an optimized Faster-RCNN to detect captured animal images in wild environments.…”

“…Feng et al. [29] developed a novel wildlife detection system based on wireless image sensor networks (WISNs) and employed an optimized Faster‐RCNN to detect captured animal images in wild environments.…”