2 research outputs found
Underwater target detection based on improved YOLOv7
Underwater target detection is a crucial aspect of ocean exploration.
However, conventional underwater target detection methods face several
challenges such as inaccurate feature extraction, slow detection speed and lack
of robustness in complex underwater environments. To address these limitations,
this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater
target detection. The proposed network utilizes an ACmixBlock module to replace
the 3x3 convolution block in the E-ELAN structure, and incorporates jump
connections and 1x1 convolution architecture between ACmixBlock modules to
improve feature extraction and network reasoning speed. Additionally, a
ResNet-ACmix module is designed to avoid feature information loss and reduce
computation, while a Global Attention Mechanism (GAM) is inserted in the
backbone and head parts of the model to improve feature extraction.
Furthermore, the K-means++ algorithm is used instead of K-means to obtain
anchor boxes and enhance model accuracy. Experimental results show that the
improved YOLOv7 network outperforms the original YOLOv7 model and other popular
underwater target detection methods. The proposed network achieved a mean
average precision (mAP) value of 89.6% and 97.4% on the URPC dataset and
Brackish dataset, respectively, and demonstrated a higher frame per second
(FPS) compared to the original YOLOv7 model. The source code for this study is
publicly available at https://github.com/NZWANG/YOLOV7-AC. In conclusion, the
improved YOLOv7 network proposed in this study represents a promising solution
for underwater target detection and holds great potential for practical
applications in various underwater tasks
Temporal Variation and Chemical Components of Rural Ambient PM2.5 during Main Agricultural Activity Periods in the Black Soil Region of Northeast China
Agricultural emissions are crucial to regional air quality in the autumn and spring due to the intense agricultural activities in Northeast China. However, information on rural ambient particulate matter (PM) in Northeast China is rare, limiting the accurate estimation of agricultural atmospheric particulate matter emissions. In this study, we monitored hourly ambient PM2.5 (PM with a diameter of less than 2.5 μm) concentrations and analyzed daily chemical components (i.e., water-soluble ions, trace elements, organic carbon, and element carbon) at a rural site in Northeast China during the autumn and spring and assessed the impact of agricultural activities on atmospheric PM2.5 concentrations. The results showed that the daily average concentrations of PM2.5 were 143 ± 109 (range: 39–539) μg m−3 from 19 October to 23 November 2017 (i.e., typical harvesting month) and 241 ± 189 (range: 97–976) μg m−3 from 1 April to 13 May 2018 (i.e., typical tilling month). In autumn, the ambient PM2.5 concentrations were high with a Southwest wind, while a Southeast wind caused high PM2.5 concentrations during spring in the rural site. The concentrations of selected water-soluble ions, trace elements, and carbonaceous fractions accounted for 33%, 4%, and 26% of PM2.5 mass concentrations, respectively, in autumn and for 10%, 5%, and 3% of PM2.5 mass concentrations, respectively, in spring. On the basis of the component analysis, straw burning, agricultural machinery, and soil dust driven by wind and tilling were the main contributors to high rural PM2.5 concentrations. In addition, the increasing coal combustion around the rural site was another important source of PM2.5