摘要

Drone-Yolo在無人機數(shù)據(jù)集上取得了巨大的成功，mAP0.5指標(biāo)上取得了顯著改進，在VisDrone2019-test上增加了13.4%，在VisDrone2019-val上增加了17.40%。這篇文章我首先復(fù)現(xiàn)Drone-Yolo，然后，在Drone-Yolo的基礎(chǔ)上加入我自己對小目標(biāo)檢測的改進。

YoloV8官方結(jié)果

YOLOv8l?summary?(fused):?268?layers,?43631280?parameters,?0?gradients,?165.0?GFLOPs
?????????????????Class?????Images??Instances??????Box(P??????????R??????mAP50??mAP50-95):?100%|██████████|?29/29?[
???????????????????all????????230???????1412??????0.922??????0.957??????0.986??????0.737
???????????????????c17????????230????????131??????0.973??????0.992??????0.995??????0.825
????????????????????c5????????230?????????68??????0.945??????????1??????0.995??????0.836
????????????helicopter????????230?????????43???????0.96??????0.907??????0.951??????0.607
??????????????????c130????????230?????????85??????0.984??????????1??????0.995??????0.655
???????????????????f16????????230?????????57??????0.955??????0.965??????0.985??????0.669
????????????????????b2????????230??????????2??????0.704??????????1??????0.995??????0.722
?????????????????other????????230?????????86??????0.903??????0.942??????0.963??????0.534
???????????????????b52????????230?????????70???????0.96??????0.971??????0.978??????0.831
??????????????????kc10????????230?????????62??????0.999??????0.984???????0.99??????0.847
???????????????command????????230?????????40???????0.97??????????1??????0.995??????0.811
???????????????????f15????????230????????123??????0.891??????????1??????0.992??????0.701
?????????????????kc135????????230?????????91??????0.971??????0.989??????0.986??????0.712
???????????????????a10????????230?????????27??????????1??????0.555??????0.899??????0.456
????????????????????b1????????230?????????20??????0.972??????????1??????0.995??????0.793
???????????????????aew????????230?????????25??????0.945??????????1???????0.99??????0.784
???????????????????f22????????230?????????17??????0.913??????????1??????0.995??????0.725
????????????????????p3????????230????????105???????0.99??????????1??????0.995??????0.801
????????????????????p8????????230??????????1??????0.637??????????1??????0.995??????0.597
???????????????????f35????????230?????????32??????0.939??????0.938??????0.978??????0.574
???????????????????f18????????230????????125??????0.985??????0.992??????0.987??????0.817
???????????????????v22????????230?????????41??????0.983??????????1??????0.995???????0.69
?????????????????su-27????????230?????????31??????0.925??????????1??????0.995??????0.859
?????????????????il-38????????230?????????27??????0.972??????????1??????0.995??????0.811
????????????????tu-134????????230??????????1??????0.663??????????1??????0.995??????0.895
?????????????????su-33????????230??????????2??????????1??????0.611??????0.995??????0.796
?????????????????an-70????????230??????????2??????0.766??????????1??????0.995???????0.73
?????????????????tu-22????????230?????????98??????0.984??????????1??????0.995??????0.831
Speed:?0.2ms?preprocess,?3.8ms?inference,?0.0ms?loss,?0.8ms?postprocess?per?image



BiC模塊

BiC模塊模塊，有三個輸入，一個輸出組成，如下圖：

我參照YoloV6中的源碼，結(jié)合YoloV8，對BiC模塊做了適當(dāng)?shù)男薷?，適應(yīng)channel的輸入和輸出，代碼如下：

class?BiFusion(nn.Module):
????'''BiFusion?Block?in?PAN'''
????def?__init__(self,?in_channels1,in_channels2,in_channels3,?out_channels):
????????super().__init__()
????????self.CV1?=?Conv(in_channels1,?out_channels,?1,?1)
????????self.CV2?=?Conv(in_channels2,?out_channels,?1,?1)
????????self.CV3?=?Conv(in_channels3,?out_channels,?1,?1)
????????self.cv_out?=?Conv(out_channels?*?3,?out_channels,?1,?1)

????????self.upsample?=?ConvTranspose(
????????????out_channels,
????????????out_channels,
????????)
????????self.downsample?=?Conv(
????????????out_channels,
????????????out_channels,
????????????3,
????????????2
????????)

????def?forward(self,?x):
????????x0?=?self.upsample(self.CV1(x[0]))
????????x1?=?self.CV2(x[1])
????????x2?=?self.downsample(self.CV3(x[2]))
????????x3=?self.cv_out(torch.cat((x0,?x1,?x2),?dim=1))
????????return?x3


改進一

測試結(jié)果

YOLOv8l?summary?(fused):?291?layers,?49370288?parameters,?0?gradients,?194.9?GFLOPs
?????????????????Class?????Images??Instances??????Box(P??????????R??????mAP50??mAP50-95):?100%|██████████|?15/15?[00:13<00:00,??1.11it/s]
???????????????????all????????230???????1412??????0.932??????0.966??????0.986??????0.735
???????????????????c17????????230????????131??????0.955??????0.992??????0.995??????0.827
????????????????????c5????????230?????????68??????0.954??????????1??????0.993??????0.829
????????????helicopter????????230?????????43??????0.902??????0.977??????0.964??????0.585
??????????????????c130????????230?????????85??????0.978??????0.941??????0.991??????0.669
???????????????????f16????????230?????????57??????0.853??????0.912??????0.957??????0.649
????????????????????b2????????230??????????2??????0.768??????????1??????0.995??????0.697
?????????????????other????????230?????????86??????0.954???????0.93??????0.972??????0.557
???????????????????b52????????230?????????70??????0.972??????0.957???????0.97??????0.794
??????????????????kc10????????230?????????62???????0.99??????0.968??????0.987??????0.845
???????????????command????????230?????????40??????0.975???????0.99??????0.975??????0.785
???????????????????f15????????230????????123??????0.948??????0.976??????0.993??????0.706
?????????????????kc135????????230?????????91??????0.965??????0.989??????0.979??????0.672
???????????????????a10????????230?????????27???????0.95??????0.697??????0.951??????0.436
????????????????????b1????????230?????????20??????0.974???????0.95??????0.988??????0.677
???????????????????aew????????230?????????25??????0.926??????????1???????0.99??????0.772
???????????????????f22????????230?????????17??????0.925??????????1??????0.995??????0.721
????????????????????p3????????230????????105??????????1??????0.977??????0.995???????0.81
????????????????????p8????????230??????????1??????0.755??????????1??????0.995??????0.697
???????????????????f35????????230?????????32??????0.967??????0.906??????0.967???????0.54
???????????????????f18????????230????????125??????0.967??????0.992??????0.992??????0.809
???????????????????v22????????230?????????41??????0.989??????????1??????0.995??????0.624
?????????????????su-27????????230?????????31??????0.982??????????1??????0.995??????0.856
?????????????????il-38????????230?????????27??????0.997??????????1??????0.995??????0.801
????????????????tu-134????????230??????????1??????0.732??????????1??????0.995??????0.995
?????????????????su-33????????230??????????2??????????1??????0.923??????0.995??????0.796
?????????????????an-70????????230??????????2??????0.809??????????1??????0.995??????0.848
?????????????????tu-22????????230?????????98???????0.99??????????1??????0.995??????0.834
Speed:?0.1ms?preprocess,?7.3ms?inference,?0.0ms?loss,?2.8ms?postprocess?per?image
Results?saved?to?runs\detect\train3


基本上沒有變化，反而增加了計算量！

改進二

測試結(jié)果

原數(shù)據(jù)集測試結(jié)果

YOLOv8l?summary?(fused):?343?layers,?46326864?parameters,?0?gradients,?244.4?GFLOPs
?????????????????Class?????Images??Instances??????Box(P??????????R??????mAP50??mAP50-95):?100%|██████████|?15/15?[00:05<00:00,??2.51it/s]
???????????????????all????????230???????1412??????0.906??????0.975??????0.986???????0.73
???????????????????c17????????230????????131??????0.954??????0.992??????0.995??????0.807
????????????????????c5????????230?????????68??????0.919??????0.997??????0.993??????0.825
????????????helicopter????????230?????????43??????0.818??????0.953??????0.954??????0.582
??????????????????c130????????230?????????85??????0.971??????0.965??????0.993??????0.655
???????????????????f16????????230?????????57??????0.829???????0.93??????0.967??????0.667
????????????????????b2????????230??????????2??????0.768??????????1??????0.995??????0.722
?????????????????other????????230?????????86??????0.881??????0.953??????0.975??????0.539
???????????????????b52????????230?????????70???????0.92??????0.943???????0.98??????0.802
??????????????????kc10????????230?????????62??????????1???????0.98??????0.989??????0.824
???????????????command????????230?????????40??????0.894??????????1??????0.983??????0.797
???????????????????f15????????230????????123??????0.926??????????1??????0.992??????0.694
?????????????????kc135????????230?????????91??????0.967??????0.989??????0.981??????0.689
???????????????????a10????????230?????????27??????0.913??????0.926??????0.935??????0.424
????????????????????b1????????230?????????20??????0.927??????????1??????0.995??????0.739
???????????????????aew????????230?????????25??????0.916??????????1??????0.995??????0.782
???????????????????f22????????230?????????17??????0.931??????????1??????0.995??????0.763
????????????????????p3????????230????????105??????0.994??????0.971??????0.995??????0.805
????????????????????p8????????230??????????1??????0.886??????????1??????0.995??????0.796
???????????????????f35????????230?????????32??????0.882??????0.875???????0.96???????0.54
???????????????????f18????????230????????125??????0.949??????0.984??????0.989???????0.81
???????????????????v22????????230?????????41??????0.968??????????1??????0.995??????0.663
?????????????????su-27????????230?????????31??????0.921??????????1??????0.995??????0.828
?????????????????il-38????????230?????????27??????0.984??????????1??????0.995??????0.803
????????????????tu-134????????230??????????1??????0.605??????????1??????0.995??????0.895
?????????????????su-33????????230??????????2??????????1???????0.86??????0.995??????0.697
?????????????????an-70????????230??????????2??????0.759??????????1??????0.995??????0.749
?????????????????tu-22????????230?????????98??????0.987??????????1??????0.995??????0.816
Speed:?0.1ms?preprocess,?6.5ms?inference,?0.0ms?loss,?0.7ms?postprocess?per?image


ViSDrone2019數(shù)據(jù)集測試結(jié)果

?????????????????Class?????Images??Instances??????Box(P??????????R??????mAP50??mAP50-95):?100%|██████████|?35/35?[00:06<00:00,??5.46it/s]
???????????????????all????????548??????38759??????0.584??????0.469??????0.491??????0.302
????????????pedestrian????????548???????8844??????0.707??????0.477??????0.572??????0.282
????????????????people????????548???????5125??????0.619??????0.425??????0.464??????0.199
???????????????bicycle????????548???????1287??????0.315??????0.282??????0.224??????0.104
???????????????????car????????548??????14064??????0.761??????0.843??????0.863??????0.631
???????????????????van????????548???????1975??????0.604??????0.498??????0.527??????0.382
?????????????????truck????????548????????750??????0.572??????0.431??????0.433????????0.3
??????????????tricycle????????548???????1045??????0.479??????0.407??????0.378??????0.219
???????awning-tricycle????????548????????532??????0.374??????0.179??????0.197???????0.12
???????????????????bus????????548????????251??????0.718??????0.614??????0.639???????0.48
?????????????????motor????????548???????4886??????0.621??????0.561???????0.58??????0.2


比論文的結(jié)果低一些，這個和batchsize以及epoch有關(guān)系！ 我選用的epoch為150，batchsize為8。如果按照論文中的300epoch可能會更高一些。

改進三

測試結(jié)果

?????????????Class?????Images??Instances??????Box(P??????????R??????mAP50??mAP50-95):?100%|██████████|?35/35?[00:19<00:00,??1.82it/s]
???????????????????all????????548??????38759???????0.57??????0.473??????0.491??????0.305
????????????pedestrian????????548???????8844??????0.678??????0.504??????0.571??????0.287
????????????????people????????548???????5125??????0.628??????0.418??????0.466????????0.2
???????????????bicycle????????548???????1287???????0.37??????0.237??????0.227??????0.112
???????????????????car????????548??????14064??????0.733??????0.852???????0.86??????0.627
???????????????????van????????548???????1975??????0.569??????0.507??????0.517??????0.376
?????????????????truck????????548????????750??????0.532??????0.437??????0.459??????0.319
??????????????tricycle????????548???????1045??????0.487??????0.395???????0.37??????0.218
???????awning-tricycle????????548????????532???????0.37??????0.212??????0.202???????0.13
???????????????????bus????????548????????251??????0.709??????0.618??????0.656???????0.49


和原來的模型相比，總體結(jié)果差不多，但是你仔細(xì)對比，發(fā)現(xiàn)，更小的目標(biāo)mAP50 更高，反而大的目標(biāo)mAP50有所降低！ ?在訓(xùn)練的過程中，驗證集已經(jīng)達到了0.512，接近作者的指標(biāo)，但是由于最后一步有融合的操作，融合后的結(jié)果有所下降！

鏈接：