An Efficient and Lightweight Pigeon Age Detection Method Based on LN-STEP-YOLO

Abstract

Deploying pigeon age detection model on edge equipment can solve the problem of video transmission delay and reduce the pressure of network transmission. Based on the deployment of edge devices, we made some improvements to You Only Look Once version 5 (YOLOv5) to form a new, lightweight, high-performance detector named LN-STEP-YOLO. In order to reduce the size of the model, we halved the number of channels in the YOLOv5s model. However, the decrease in the number of channels brings some problems, such as low global information acquisition, retention of image redundancy information, attention deficit, etc. To solve these problems, we did the following work. First, we proposed a new convolution structure with the effect of a large convolution kernel, StepConv. Second, a $2\times 2$ convolution with step size 2 was used at the input to split each image into individual patches. Third, External Attention (EA) was introduced in the bottleneck structure. Fourth, a modified extremely separated convolutional block (XsepConv) was used for downsampling. Finally, we replaced the batch normalization (BN) of all non-downsampled layers with layer normalization (LN). The results showed that the improved algorithm outperformed generic lightweight networks such as Mixnet, Mobilenetv3, and Ghostnet to distinguish small-sized, overlapping pigeons, achieving 92.8% mean average precision (mAP) at about 17% of YOLOv5s parameters, 0.1% lower than that achieved by use of YOLOv5s. In addition, the improved method had 3.7G floating point operations (FLOPs) and 1.25G parameters, which allowed the detection of the growth stages of pigeons in real environments and provided a reference to guide placement of feeders in automated pigeon farming.

Keywords

• Pigeon
• accurate feeding
• overlap
• greater receptive field
• layer normalization