Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386814316> ?p ?o ?g. }
- W4386814316 endingPage "10397" @default.
- W4386814316 startingPage "10397" @default.
- W4386814316 abstract "Drones are widely used for wildlife monitoring. Deep learning algorithms are key to the success of monitoring wildlife with drones, although they face the problem of detecting small targets. To solve this problem, we have introduced the SE-YOLO model, which incorporates a channel self-attention mechanism into the advanced real-time object detection algorithm YOLOv7, enabling the model to perform effectively on small targets. However, there is another barrier; the lack of publicly available UAV wildlife aerial datasets hampers research on UAV wildlife monitoring algorithms. To fill this gap, we present a large-scale, multi-class, high-quality dataset called WAID (Wildlife Aerial Images from Drone), which contains 14,375 UAV aerial images from different environmental conditions, covering six wildlife species and multiple habitat types. We conducted a statistical analysis experiment, an algorithm detection comparison experiment, and a dataset generalization experiment. The statistical analysis experiment demonstrated the dataset characteristics both quantitatively and intuitively. The comparison and generalization experiments compared different types of advanced algorithms as well as the SE-YOLO method from the perspective of the practical application of UAVs for wildlife monitoring. The experimental results show that WAID is suitable for the study of wildlife monitoring algorithms for UAVs, and SE-YOLO is the most effective in this scenario, with a mAP of up to 0.983. This study brings new methods, data, and inspiration to the field of wildlife monitoring by UAVs." @default.
- W4386814316 created "2023-09-18" @default.
- W4386814316 creator A5009995771 @default.
- W4386814316 creator A5062077940 @default.
- W4386814316 creator A5062892351 @default.
- W4386814316 creator A5085034937 @default.
- W4386814316 date "2023-09-17" @default.
- W4386814316 modified "2023-09-26" @default.
- W4386814316 title "WAID: A Large-Scale Dataset for Wildlife Detection with Drones" @default.
- W4386814316 cites W1536680647 @default.
- W4386814316 cites W1861492603 @default.
- W4386814316 cites W1978993121 @default.
- W4386814316 cites W2015048026 @default.
- W4386814316 cites W2102605133 @default.
- W4386814316 cites W2102812780 @default.
- W4386814316 cites W2108598243 @default.
- W4386814316 cites W2109255472 @default.
- W4386814316 cites W2127172520 @default.
- W4386814316 cites W2143897835 @default.
- W4386814316 cites W2162438516 @default.
- W4386814316 cites W2165900125 @default.
- W4386814316 cites W2183341477 @default.
- W4386814316 cites W2265713170 @default.
- W4386814316 cites W2310940884 @default.
- W4386814316 cites W2314029052 @default.
- W4386814316 cites W2331725058 @default.
- W4386814316 cites W2395579298 @default.
- W4386814316 cites W2510497028 @default.
- W4386814316 cites W2527949113 @default.
- W4386814316 cites W2587816047 @default.
- W4386814316 cites W2594258618 @default.
- W4386814316 cites W2609402060 @default.
- W4386814316 cites W2725546079 @default.
- W4386814316 cites W2748369121 @default.
- W4386814316 cites W2798355657 @default.
- W4386814316 cites W2883423620 @default.
- W4386814316 cites W2884276823 @default.
- W4386814316 cites W2886934227 @default.
- W4386814316 cites W2909440372 @default.
- W4386814316 cites W2914187338 @default.
- W4386814316 cites W2929565236 @default.
- W4386814316 cites W2948995641 @default.
- W4386814316 cites W2962749812 @default.
- W4386814316 cites W2963037989 @default.
- W4386814316 cites W2963420686 @default.
- W4386814316 cites W2963456480 @default.
- W4386814316 cites W2963786238 @default.
- W4386814316 cites W2995926063 @default.
- W4386814316 cites W2997508096 @default.
- W4386814316 cites W3034307881 @default.
- W4386814316 cites W3034552520 @default.
- W4386814316 cites W3035263170 @default.
- W4386814316 cites W3036152936 @default.
- W4386814316 cites W3043257208 @default.
- W4386814316 cites W3047385447 @default.
- W4386814316 cites W3047401433 @default.
- W4386814316 cites W3088311973 @default.
- W4386814316 cites W3094897602 @default.
- W4386814316 cites W3106250896 @default.
- W4386814316 cites W3126088455 @default.
- W4386814316 cites W3138755759 @default.
- W4386814316 cites W3170539225 @default.
- W4386814316 cites W3176230123 @default.
- W4386814316 cites W3192821584 @default.
- W4386814316 cites W3194254656 @default.
- W4386814316 cites W3201753961 @default.
- W4386814316 cites W3209590562 @default.
- W4386814316 cites W3215216361 @default.
- W4386814316 cites W4224285690 @default.
- W4386814316 cites W4224319240 @default.
- W4386814316 cites W4285132074 @default.
- W4386814316 cites W4285804308 @default.
- W4386814316 cites W4289792858 @default.
- W4386814316 cites W4290879060 @default.
- W4386814316 cites W4309279743 @default.
- W4386814316 cites W4366380409 @default.
- W4386814316 cites W4384929698 @default.
- W4386814316 cites W4386076325 @default.
- W4386814316 cites W73112891 @default.
- W4386814316 doi "https://doi.org/10.3390/app131810397" @default.
- W4386814316 hasPublicationYear "2023" @default.
- W4386814316 type Work @default.
- W4386814316 citedByCount "0" @default.
- W4386814316 crossrefType "journal-article" @default.
- W4386814316 hasAuthorship W4386814316A5009995771 @default.
- W4386814316 hasAuthorship W4386814316A5062077940 @default.
- W4386814316 hasAuthorship W4386814316A5062892351 @default.
- W4386814316 hasAuthorship W4386814316A5085034937 @default.
- W4386814316 hasBestOaLocation W43868143161 @default.
- W4386814316 hasConcept C124101348 @default.
- W4386814316 hasConcept C134306372 @default.
- W4386814316 hasConcept C154945302 @default.
- W4386814316 hasConcept C177148314 @default.
- W4386814316 hasConcept C18903297 @default.
- W4386814316 hasConcept C205649164 @default.
- W4386814316 hasConcept C2778755073 @default.
- W4386814316 hasConcept C29376679 @default.
- W4386814316 hasConcept C33923547 @default.