TY - JOUR
T1 - Insect real-time detection in complex environments based on improved YOLOV3
AU - Juan Wang
JO - Journal of Information and Computing Science
VL - 4
SP - 291
EP - 299
PY - 2019
DA - 2019/12
SN - 14
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22405.html
KW - 
AB - 1 College of Information and Science Technology, Jinan University, 
Guangzhou, 51000, China 
(Received November 02 2019, accepted December 28 2019) 
 The  combination  of  advanced  computer  vision  technology  and  insect  image  recognition 
technology can be effectively applied to environmental monitoring, pest diagnosis, epidemiology and other 
fields. However, accurate location and classification of relatively small insects in complex scenes has always 
been a difficult problem for this technology. Although YOLOv3 combines deep features with shallow features 
to facilitate the detection of small objects, experiments have found that YOLOv3 has more undetected cases 
for small target insects in complex backgrounds. In order to solve this problem, this paper optimizes YOLOv3 
algorithm. Firstly, the SE blocks are embedded into YOLOv3 network to learn global features and enhance the 
expression ability of feature maps, so that the network can detect more objects. Because YOLOv3 itself has a 
complex network structure and the amount of parameters and calculations are increased after embedding the 
SE  block,  so  this  paper  also  uses  depthwise  separable  convolutions,  which  greatly  reduces  the  amount  of 
parameters and computation under the condition of little loss of accuracy, thus improving the detection speed. 
Training and testing on the insect dataset made in this paper, the original YOLOv3 runs at 33 f / s, and the 
mean Average Precision (mAP) is only 86.8%, While the improved YOLOv3 runs at 38 f / s, the mAP reaches 
90.6%. The improved algorithm can detect more targets, reduce the omission factor and improve the detection 
speed.